Survival analysis in patients with HCC treated with Systemic Therapies (n=134)
Sultan Alharbi
2025-11-23
# Load the data
# Load the data
ClinicData <- read.csv("/Users/sultanalharbi/Library/CloudStorage/OneDrive-Personal/Projects/Thesis_Chapters/Chapter 2 (Prognostic Indicators for HCC)/ICI_TKI_Ram_Sul_Mat_2025-01-29_Final.csv")
# Set the options to limit the output
options(max.print=100
)
# Print the summary of the data
summary(ClinicData)## Radio_ID Lab.ID_Baseline Lab.ID_Baseline2 Specific.Treatment
## Length:134 Length:134 Length:134 Length:134
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Treatment_Class Gender Gender_Rank Age
## Length:134 Length:134 Min. :1.000 Min. :37.00
## Class :character Class :character 1st Qu.:1.000 1st Qu.:63.00
## Mode :character Mode :character Median :1.000 Median :70.50
## Aetiology CTP PS Cirrhosis
## Length:134 Length:134 Min. :0.0000 Length:134
## Class :character Class :character 1st Qu.:0.0000 Class :character
## Mode :character Mode :character Median :1.0000 Mode :character
## Best_Responsce tMAD cfDNA_Level_Baseline_ng_ml
## Length:134 Min. :0.005713 Min. : 20.0
## Class :character 1st Qu.:0.010032 1st Qu.: 439.2
## Mode :character Median :0.013982 Median : 1010.0
## BCLC AFP PVT
## Length:134 Min. : 1.0 Length:134
## Class :character 1st Qu.: 4.0 Class :character
## Mode :character Median : 35.5 Mode :character
## Tumour.largest.size..cm. Number.of.Nodules Time_OS OS_Status
## Min. : 1.000 Length:134 Min. : 1.520 Min. :0.0000
## 1st Qu.: 3.000 Class :character 1st Qu.: 7.183 1st Qu.:0.2500
## Median : 4.650 Mode :character Median :14.485 Median :1.0000
## Time_PFS PFS_Status DELFI_Baseline DELFI_Baseline_Rank
## Min. : 0.330 Min. :0.0000 Min. :0.1240 Min. :0.0000
## 1st Qu.: 3.240 1st Qu.:1.0000 1st Qu.:0.2313 1st Qu.:0.0000
## Median : 7.550 Median :1.0000 Median :0.2840 Median :0.0000
## Tumour_Fraction_ichorCNA_Baseline TF_Rank
## Min. :0.00900 Min. :0.0000
## 1st Qu.:0.01300 1st Qu.:0.0000
## Median :0.04100 Median :0.0000
## [ reached 'max' / getOption("max.print") -- omitted 3 rows ]Here is the Kaplan-Meier curve for the overall survival (OS) of whole patient cohort with HCC treated with Systemic therapies.
# Fit the survival model
Whole_Cohort_OS <- survfit(Surv(Time_OS, OS_Status) ~ 1, data = ClinicData, conf.type = "log-log")
summary_data_Whole_Cohort_OS <- summary(Whole_Cohort_OS)
Whole_Cohort_median_OS <- summary_data_Whole_Cohort_OS$table['median']
Whole_Cohort_lower_OS_ci <- summary_data_Whole_Cohort_OS$table['0.95LCL']
Whole_Cohort_upper_OS_ci <- summary_data_Whole_Cohort_OS$table['0.95UCL']
# Create the survival plot
OS_plot <- ggsurvplot(Whole_Cohort_OS,
conf.int = TRUE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
tables.y.text.col = TRUE,
fontsize = 5,
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.25,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "OS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median OS and confidence interval
OS_plot$plot <- OS_plot$plot +
geom_label(aes(x = 35, y = 0.5,
label = paste("Median OS: ", round(Whole_Cohort_median_OS, 2), " months\n",
"95% CI: ", round(Whole_Cohort_lower_OS_ci, 2),"-", round(Whole_Cohort_upper_OS_ci, 2))),
size = 6, hjust = 0, vjust = 0.5)
# Print the plot in the R Markdown document
print(OS_plot)
# Save the plot as a high-resolution PNG
png("Whole_Cohort_OS_TKI_ICI.png", width = 8, height = 6, units = "in", res = 500)
print(OS_plot)
dev.off()## quartz_off_screen
## 2Here is the Kaplan-Meier curve for the Pregression-Free survival (PFS) of whole patient cohort with HCC treated with Systemic therapies.
# Fit the survival model
Whole_Cohort_PFS <- survfit(Surv(Time_PFS, PFS_Status) ~ 1, data = ClinicData, conf.type = "log-log")
summary_data_Whole_Cohort_PFS <- summary(Whole_Cohort_PFS)
Whole_Cohort_median_PFS <- summary_data_Whole_Cohort_PFS$table['median']
Whole_Cohort_lower_PFS_ci <- summary_data_Whole_Cohort_PFS$table['0.95LCL']
Whole_Cohort_upper_PFS_ci <- summary_data_Whole_Cohort_PFS$table['0.95UCL']
# Create the survival plot
PFS_plot <- ggsurvplot(Whole_Cohort_PFS,
conf.int = TRUE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
tables.y.text.col = TRUE,
fontsize = 5,
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.25,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "PFS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median PFS and confidence interval
PFS_plot$plot <- PFS_plot$plot +
geom_label(aes(x = 25, y = 0.5,
label = paste("Median PFS: ", round(Whole_Cohort_median_PFS, 2), " months\n",
"95% CI: ", round(Whole_Cohort_lower_PFS_ci, 2),"-", round(Whole_Cohort_upper_PFS_ci, 2))),
size = 6, hjust = 0, vjust = 0.5)
# Print the plot in the R Markdown document
print(PFS_plot)
# Save the plot as a high-resolution PNG
png("Whole_Cohort_PFS_TKI_ICI.png", width = 8, height = 6, units = "in", res = 300)
print(PFS_plot)
dev.off()## quartz_off_screen
## 2# Fit the survival model for OS
BCLC_OS <- survfit(Surv(Time_OS, OS_Status) ~ BCLC, data = ClinicData, conf.type = "log-log")
summary_data_BCLC_OS <- summary(BCLC_OS)
# Extract median OS and confidence intervals for each group
BCLC_median_OS_B <- summary_data_BCLC_OS$time[summary_data_BCLC_OS$strata == "BCLC=B" & summary_data_BCLC_OS$time == summary_data_BCLC_OS$table["BCLC=B", "median"]]
BCLC_lower_OS_ci_B <- summary_data_BCLC_OS$table["BCLC=B", "0.95LCL"]
BCLC_upper_OS_ci_B <- summary_data_BCLC_OS$table["BCLC=B", "0.95UCL"]
BCLC_median_OS_C <- summary_data_BCLC_OS$time[summary_data_BCLC_OS$strata == "BCLC=C" & summary_data_BCLC_OS$time == summary_data_BCLC_OS$table["BCLC=C", "median"]]
BCLC_lower_OS_ci_C <- summary_data_BCLC_OS$table["BCLC=C", "0.95LCL"]
BCLC_upper_OS_ci_C <- summary_data_BCLC_OS$table["BCLC=C", "0.95UCL"]
# Create the survival plot
BCLC_OS_plot <- ggsurvplot(BCLC_OS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(50, 0.30), # <- move the "log-rank" method label here
pval.coord = c(50, 0.20), # <- move the log-rank p-value here (x, y)
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("B", "C"), # change group labels
tables.col = "BCLC",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "BCLC", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "OS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median OS and confidence interval for group
BCLC_OS_plot$plot <- BCLC_OS_plot$plot +
geom_label(aes(x = 37, y = 0.60,
label = paste("C \nMedian OS: ", round(BCLC_median_OS_C, 2), " months\n",
"95% CI: ", round(BCLC_lower_OS_ci_C, 2), "-", round(BCLC_upper_OS_ci_C, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 37, y = 0.90,
label = paste("B \nMedian OS: ", round(BCLC_median_OS_B, 2), " months\n",
"95% CI: ", round(BCLC_lower_OS_ci_B, 2), "-", round(BCLC_upper_OS_ci_B, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")
# Print the plot in the R Markdown document
print(BCLC_OS_plot)
# High-resolution plot saving
png("BCLC_OS_TKI_ICI.png", width = 8, height = 6, units = "in", res = 500)
print(BCLC_OS_plot)
dev.off()## quartz_off_screen
## 2# Fit the survival model for PFS
BCLC_PFS <- survfit(Surv(Time_PFS, PFS_Status) ~ BCLC, data = ClinicData, conf.type = "log-log")
summary_data_BCLC_PFS <- summary(BCLC_PFS)
# Extract median PFS and confidence intervals for each group
BCLC_median_PFS_B <- summary_data_BCLC_PFS$time[summary_data_BCLC_PFS$strata == "BCLC=B" & summary_data_BCLC_PFS$time == summary_data_BCLC_PFS$table["BCLC=B", "median"]]
BCLC_lower_PFS_ci_B <- summary_data_BCLC_PFS$table["BCLC=B", "0.95LCL"]
BCLC_upper_PFS_ci_B <- summary_data_BCLC_PFS$table["BCLC=B", "0.95UCL"]
BCLC_median_PFS_C <- summary_data_BCLC_PFS$time[summary_data_BCLC_PFS$strata == "BCLC=C" & summary_data_BCLC_PFS$time == summary_data_BCLC_PFS$table["BCLC=C", "median"]]
BCLC_lower_PFS_ci_C <- summary_data_BCLC_PFS$table["BCLC=C", "0.95LCL"]
BCLC_upper_PFS_ci_C <- summary_data_BCLC_PFS$table["BCLC=C", "0.95UCL"]
# Create the survival plot
BCLC_PFS_plot <- ggsurvplot(BCLC_PFS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(30, 0.30), # <- move the "log-rank" method label here
pval.coord = c(30, 0.20), # <- move the log-rank p-value here (x, y)
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("B", "C"), # change group labels
tables.col = "BCLC",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "BCLC", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "PFS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median PFS and confidence interval for group
BCLC_PFS_plot$plot <- BCLC_PFS_plot$plot +
geom_label(aes(x = 24, y = 0.60,
label = paste("C \nMedian PFS: ", round(BCLC_median_PFS_C, 2), " months\n",
"95% CI: ", round(BCLC_lower_PFS_ci_C, 2), "-", round(BCLC_upper_PFS_ci_C, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 24, y = 0.90,
label = paste("B \nMedian PFS: ", round(BCLC_median_PFS_B, 2), " months\n",
"95% CI: ", round(BCLC_lower_PFS_ci_B, 2), "-", round(BCLC_upper_PFS_ci_B, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")
# Print the plot in the R Markdown document
print(BCLC_PFS_plot)
# High-resolution plot saving
png("BCLC_PFS_TKI_ICI.png", width = 8, height = 6, units = "in", res = 500)
print(BCLC_PFS_plot)
dev.off()## quartz_off_screen
## 2# Fit the survival model for OS
CTP_OS <- survfit(Surv(Time_OS, OS_Status) ~ CTP, data = ClinicData, conf.type = "log-log")
summary_data_CTP_OS <- summary(CTP_OS)
# Extract median OS and confidence intervals for each group
CTP_median_OS_A <- summary_data_CTP_OS$table["CTP=A" , "median"]
CTP_lower_OS_ci_A <- summary_data_CTP_OS$table["CTP=A", "0.95LCL"]
CTP_upper_OS_ci_A <- summary_data_CTP_OS$table["CTP=A", "0.95UCL"]
CTP_median_OS_B <- summary_data_CTP_OS$table["CTP=B", "median"]
CTP_lower_OS_ci_B <- summary_data_CTP_OS$table["CTP=B", "0.95LCL"]
CTP_upper_OS_ci_B <- summary_data_CTP_OS$table["CTP=B", "0.95UCL"]
# Create the survival plot
CTP_OS_plot <- ggsurvplot(CTP_OS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(40, 0.30), # <- move the "log-rank" method label here
pval.coord = c(40, 0.20), # <- move the log-rank p-value here (x, y)
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("A", "B"), # change group labels
tables.col = "CTP",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "CTP Class", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "OS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median OS and confidence interval for group
CTP_OS_plot$plot <- CTP_OS_plot$plot +
geom_label(aes(x = 35, y = 0.60,
label = paste("B \nMedian OS: ", round(CTP_median_OS_B, 2), " months\n",
"95% CI: ", round(CTP_lower_OS_ci_B, 2), "-", round(CTP_upper_OS_ci_B, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 35, y = 0.90,
label = paste("A \nMedian OS: ", round(CTP_median_OS_A, 2), " months\n",
"95% CI: ", round(CTP_lower_OS_ci_A, 2), "-", round(CTP_upper_OS_ci_A, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")
# Print the plot in the R Markdown document
print(CTP_OS_plot)
# High-resolution plot saving
png("CTP_OS_TKI_ICI.png", width = 8, height = 6, units = "in", res = 500)
print(CTP_OS_plot)
dev.off()## quartz_off_screen
## 2# Fit the survival model for PFS
CTP_PFS <- survfit(Surv(Time_PFS, PFS_Status) ~ CTP, data = ClinicData, conf.type = "log-log")
summary_data_CTP_PFS <- summary(CTP_PFS)
# Extract median PFS and confidence intervals for each group
CTP_median_PFS_A <- summary_data_CTP_PFS$table["CTP=A" , "median"]
CTP_lower_PFS_ci_A <- summary_data_CTP_PFS$table["CTP=A", "0.95LCL"]
CTP_upper_PFS_ci_A <- summary_data_CTP_PFS$table["CTP=A", "0.95UCL"]
CTP_median_PFS_B <- summary_data_CTP_PFS$table["CTP=B", "median"]
CTP_lower_PFS_ci_B <- summary_data_CTP_PFS$table["CTP=B", "0.95LCL"]
CTP_upper_PFS_ci_B <- summary_data_CTP_PFS$table["CTP=B", "0.95UCL"]
# Create the survival plot
CTP_PFS_plot <- ggsurvplot(
CTP_PFS,
conf.int = FALSE,
pval = TRUE,
pval.method = TRUE,
pval.size = 4,
pval.method.coord = c(30, 0.35), # <- move the "log-rank" method label here
pval.coord = c(30, 0.25), # <- move the log-rank p-value here (x, y)
test.for.trend = FALSE,
risk.table = TRUE,
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv",
legend.labs = c("A","B"),
tables.col = "CTP",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "CTP Class",
risk.table.title = "Number at risk",
title = NULL,
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "PFS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15, font.y = 15, font.legend.title = 15
)
# Annotate the median PFS and confidence interval for group
CTP_PFS_plot$plot <- CTP_PFS_plot$plot +
geom_label(aes(x = 25, y = 0.70,
label = paste("B \nMedian PFS: ", round(CTP_median_PFS_B, 2), " months\n",
"95% CI: ", round(CTP_lower_PFS_ci_B, 2), "-", round(CTP_upper_PFS_ci_B, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 25, y = 0.98,
label = paste("A \nMedian PFS: ", round(CTP_median_PFS_A, 2), " months\n",
"95% CI: ", round(CTP_lower_PFS_ci_A, 2), "-", round(CTP_upper_PFS_ci_A, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")
# Print the plot in the R Markdown document
print(CTP_PFS_plot)
# High-resolution plot saving
png("CTP_PFS_TKI_ICI.png", width = 8, height = 6, units = "in", res = 500)
print(CTP_PFS_plot)
dev.off()## quartz_off_screen
## 2# Fit the survival model for OS
PS_OS <- survfit(Surv(Time_OS, OS_Status) ~ PS, data = ClinicData, conf.type = "log-log")
summary_data_PS_OS <- summary(PS_OS)
# Extract median OS and confidence intervals for each group
PS_median_OS_0 <- summary_data_PS_OS$table["PS=0" , "median"]
PS_lower_OS_ci_0 <- summary_data_PS_OS$table["PS=0", "0.95LCL"]
PS_upper_OS_ci_0 <- summary_data_PS_OS$table["PS=0", "0.95UCL"]
PS_median_OS_1 <- summary_data_PS_OS$table["PS=1", "median"]
PS_lower_OS_ci_1 <- summary_data_PS_OS$table["PS=1", "0.95LCL"]
PS_upper_OS_ci_1 <- summary_data_PS_OS$table["PS=1", "0.95UCL"]
PS_median_OS_2 <- summary_data_PS_OS$table["PS=2", "median"]
PS_lower_OS_ci_2 <- summary_data_PS_OS$table["PS=2", "0.95LCL"]
PS_upper_OS_ci_2 <- summary_data_PS_OS$table["PS=2", "0.95UCL"]
# Create the survival plot
PS_OS_plot <- ggsurvplot(PS_OS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(45, 0.30), # <- move the "log-rank" method label here
pval.coord = c(45, 0.25),
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("0", "1", "2"), # change group labels
tables.col = "PS",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "ECOG-PS", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.20,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "OS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median OS and confidence interval for group
PS_OS_plot$plot <- PS_OS_plot$plot +
geom_label(aes(x = 43, y = 0.90,
label = paste("0 \nMedian OS: ", round(PS_median_OS_0, 2), " months\n",
"95% CI: ", round(PS_lower_OS_ci_0, 2), "-", round(PS_upper_OS_ci_0, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D") +
geom_label(aes(x = 43, y = 0.70,
label = paste("1 \nMedian OS: ", round(PS_median_OS_1, 2), " months\n",
"95% CI: ", round(PS_lower_OS_ci_1, 2), "-", round(PS_upper_OS_ci_1, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BA38") +
geom_label(aes(x = 43, y = 0.50,
label = paste("2 \nMedian OS: ", round(PS_median_OS_2, 2), " months\n",
"95% CI: ", round(PS_lower_OS_ci_2, 2), "-", round(PS_upper_OS_ci_2, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#6495ED")
# Print the plot in the R Markdown document
print(PS_OS_plot)
# High-resolution plot saving
png("PS_OS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(PS_OS_plot)
dev.off()## quartz_off_screen
## 2# Fit the survival model for PFS
PS_PFS <- survfit(Surv(Time_PFS, PFS_Status) ~ PS, data = ClinicData, conf.type = "log-log")
summary_data_PS_PFS <- summary(PS_PFS)
# Extract median PFS and confidence intervals for each group
PS_median_PFS_0 <- summary_data_PS_PFS$table["PS=0" , "median"]
PS_lower_PFS_ci_0 <- summary_data_PS_PFS$table["PS=0", "0.95LCL"]
PS_upper_PFS_ci_0 <- summary_data_PS_PFS$table["PS=0", "0.95UCL"]
PS_median_PFS_1 <- summary_data_PS_PFS$table["PS=1", "median"]
PS_lower_PFS_ci_1 <- summary_data_PS_PFS$table["PS=1", "0.95LCL"]
PS_upper_PFS_ci_1 <- summary_data_PS_PFS$table["PS=1", "0.95UCL"]
PS_median_PFS_2 <- summary_data_PS_PFS$table["PS=2", "median"]
PS_lower_PFS_ci_2 <- summary_data_PS_PFS$table["PS=2", "0.95LCL"]
PS_upper_PFS_ci_2 <- summary_data_PS_PFS$table["PS=2", "0.95UCL"]
# Create the survival plot
PS_PFS_plot <- ggsurvplot(PS_PFS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(25, 0.22), # <- move the "log-rank" method label here
pval.coord = c(25, 0.18),
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("0", "1", "2"), # change group labels
tables.col = "PS",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "ECOG-PS", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.20,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "PFS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median PFS and confidence interval for group
PS_PFS_plot$plot <- PS_PFS_plot$plot +
geom_label(aes(x = 25, y = 0.80,
label = paste("0 \nMedian PFS: ", round(PS_median_PFS_0, 2), " months\n",
"95% CI: ", round(PS_lower_PFS_ci_0, 2), "-", round(PS_upper_PFS_ci_0, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D") +
geom_label(aes(x = 25, y = 0.60,
label = paste("1 \nMedian PFS: ", round(PS_median_PFS_1, 2), " months\n",
"95% CI: ", round(PS_lower_PFS_ci_1, 2), "-", round(PS_upper_PFS_ci_1, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BA38") +
geom_label(aes(x = 25, y = 0.40,
label = paste("2 \nMedian PFS: ", round(PS_median_PFS_2, 2), " months\n",
"95% CI: ", round(PS_lower_PFS_ci_2, 2), "-", round(PS_upper_PFS_ci_2, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#6495ED")
# Print the plot in the R Markdown document
print(PS_PFS_plot)
# High-resolution plot saving
png("PS_PFS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(PS_PFS_plot)
dev.off()## quartz_off_screen
## 2# Fit the survival model for OS
HAP_OS <- survfit(Surv(Time_OS, OS_Status) ~ HAP_class, data = ClinicData)## Error in eval(predvars, data, env): object 'HAP_class' not found## Error: object 'HAP_OS' not found# Extract median OS and confidence intervals for each group
HAP_median_OS_A <- summary_data_HAP_OS$table["HAP_class=A" , "median"]## Error: object 'summary_data_HAP_OS' not found## Error: object 'summary_data_HAP_OS' not found## Error: object 'summary_data_HAP_OS' not found## Error: object 'summary_data_HAP_OS' not found## Error: object 'summary_data_HAP_OS' not found## Error: object 'summary_data_HAP_OS' not found## Error: object 'summary_data_HAP_OS' not found## Error: object 'summary_data_HAP_OS' not found## Error: object 'summary_data_HAP_OS' not found## Error: object 'summary_data_HAP_OS' not found## Error: object 'summary_data_HAP_OS' not found## Error: object 'summary_data_HAP_OS' not found# Create the survival plot
HAP_OS_plot <- ggsurvplot(HAP_OS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("A", "B", "C", "D"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "HAP Class", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.20,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "OS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15,
palette = c("#F8766D", "#00BA38", "#619CFF", "#D161C4")) # Match line colors## Error: object 'HAP_OS' not found# Annotate the median OS and confidence interval for each group
HAP_OS_plot$plot <- HAP_OS_plot$plot +
annotate("label", x = 48, y = 1,
label = paste("A\nMedian OS: ", round(HAP_median_OS_A, 2), " months\n",
"95% CI: ", round(HAP_lower_OS_ci_A, 2), "-", round(HAP_upper_OS_ci_A, 2)),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D") +
annotate("label", x = 48, y = 0.80,
label = paste("B\nMedian OS: ", round(HAP_median_OS_B, 2), " months\n",
"95% CI: ", round(HAP_lower_OS_ci_B, 2), "-", round(HAP_upper_OS_ci_B, 2)),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BA38") +
annotate("label", x = 48, y = 0.60,
label = paste("C\nMedian OS: ", round(HAP_median_OS_C, 2), " months\n",
"95% CI: ", round(HAP_lower_OS_ci_C, 2), "-", round(HAP_upper_OS_ci_C, 2)),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#619CFF") +
annotate("label", x = 48, y = 0.40,
label = paste("D\nMedian OS: ", round(HAP_median_OS_D, 2), " months\n",
"95% CI: ", round(HAP_lower_OS_ci_D, 2), "-", round(HAP_upper_OS_ci_D, 2)),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#D161C4")## Error: object 'HAP_OS_plot' not found## Error: object 'HAP_OS_plot' not found# High-resolution plot saving
png("HAP_OS_TKIs.png", width = 10, height = 8, units = "in", res = 300)
print(HAP_OS_plot)## Error: object 'HAP_OS_plot' not found## quartz_off_screen
## 2# Fit the survival model for PFS
HAP_PFS <- survfit(Surv(Time_PFS, PFS_Status) ~ HAP_class, data = ClinicData)## Error in eval(predvars, data, env): object 'HAP_class' not found## Error: object 'HAP_PFS' not found# Extract median OS and confidence intervals for each group
HAP_median_PFS_A <- summary_data_HAP_PFS$table["HAP_class=A" , "median"]## Error: object 'summary_data_HAP_PFS' not found## Error: object 'summary_data_HAP_PFS' not found## Error: object 'summary_data_HAP_PFS' not found## Error: object 'summary_data_HAP_PFS' not found## Error: object 'summary_data_HAP_PFS' not found## Error: object 'summary_data_HAP_PFS' not found## Error: object 'summary_data_HAP_PFS' not found## Error: object 'summary_data_HAP_PFS' not found## Error: object 'summary_data_HAP_PFS' not found## Error: object 'summary_data_HAP_PFS' not found## Error: object 'summary_data_HAP_PFS' not found## Error: object 'summary_data_HAP_PFS' not found# Create the survival plot
HAP_PFS_plot <- ggsurvplot(HAP_PFS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("A", "B", "C", "D"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "HAP Class", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.20,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "PFS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15,
palette = c("#F8766D", "#00BA38", "#619CFF", "#D161C4")) # Match line colors## Error: object 'HAP_PFS' not found# Annotate the median PFS and confidence interval for each group
HAP_PFS_plot$plot <- HAP_PFS_plot$plot +
annotate("label", x = 30, y = 1,
label = paste("A\nMedian PFS: ", round(HAP_median_PFS_A, 2), " months\n",
"95% CI: ", round(HAP_lower_PFS_ci_A, 2), "-", round(HAP_upper_PFS_ci_A, 2)),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D") +
annotate("label", x = 30, y = 0.80,
label = paste("B\nMedian PFS: ", round(HAP_median_PFS_B, 2), " months\n",
"95% CI: ", round(HAP_lower_PFS_ci_B, 2), "-", round(HAP_upper_PFS_ci_B, 2)),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BA38") +
annotate("label", x = 30, y = 0.60,
label = paste("C\nMedian PFS: ", round(HAP_median_PFS_C, 2), " months\n",
"95% CI: ", round(HAP_lower_PFS_ci_C, 2), "-", round(HAP_upper_PFS_ci_C, 2)),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#619CFF") +
annotate("label", x = 30, y = 0.40,
label = paste("D\nMedian PFS: ", round(HAP_median_PFS_D, 2), " months\n",
"95% CI: ", round(HAP_lower_PFS_ci_D, 2), "-", round(HAP_upper_PFS_ci_D, 2)),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#D161C4")## Error: object 'HAP_PFS_plot' not found## Error: object 'HAP_PFS_plot' not found# High-resolution plot saving
png("HAP_PFS_TKIs.png", width = 11, height = 8, units = "in", res = 300)
print(HAP_PFS_plot)## Error: object 'HAP_PFS_plot' not found## quartz_off_screen
## 2#### Cirrhosis Analysis
###For Cirrhosis survival curve the right hand side should be ~ Cirrhosis
Cirrhosis_OS = survfit(Surv(Time_OS, OS_Status) ~ Cirrhosis , data = ClinicData)
summary_data_Cirrhosis_OS <- summary(Cirrhosis_OS)
# Extract median OS and confidence intervals for each group
Cirrhosis_median_OS_NO <- summary_data_Cirrhosis_OS$table["Cirrhosis=NO", "median"]
Cirrhosis_lower_OS_ci_NO <- summary_data_Cirrhosis_OS$table["Cirrhosis=NO", "0.95LCL"]
Cirrhosis_upper_OS_ci_NO <- summary_data_Cirrhosis_OS$table["Cirrhosis=NO", "0.95UCL"]
Cirrhosis_median_OS_YES <- summary_data_Cirrhosis_OS$table["Cirrhosis=YES", "median"]
Cirrhosis_lower_OS_ci_YES <- summary_data_Cirrhosis_OS$table["Cirrhosis=YES", "0.95LCL"]
Cirrhosis_upper_OS_ci_YES <- summary_data_Cirrhosis_OS$table["Cirrhosis=YES", "0.95UCL"]
Cirrhosis_OS_plot <- ggsurvplot(Cirrhosis_OS,
conf.int=FALSE, # add confidence intervals
pval=TRUE, # show the p-value for the log-rank test
pval.method = TRUE, #corresponds to survival curves' comparison
pval.size= 4,# numeric value specifying the p-value text size. Default is 5.
test.for.trend = FALSE,
risk.table=TRUE,# show a risk table below the plot.
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", #for drawing a horizontal/vertical line at median survival.
legend.labs=c("No", "Yes"), # change group labels
tables.col = "strata",
tables.y.text.col=TRUE,
fontsize=5,
legend.title="Cirrhosis", # add legend title
risk.table.title="Number at risk", #the title to be used for the risk table.
title=NULL, # add title to plot
risk.table.height=.25,
add.all = FALSE,
combine = FALSE,
xlab="Time, months",
ylab="OS Probability",
axes.offset=TRUE,
surv.plot.height=1,
font.x = 15,
font.y = 15,
font.legend.title=15)## Error in ggsurvplot_df(d, fun = fun, color = color, palette = palette, : The length of legend.labs should be 3# Annotate the median OS and confidence interval for group
Cirrhosis_OS_plot$plot <- Cirrhosis_OS_plot$plot +
geom_label(aes(x = 37, y = 0.60,
label = paste("NO \nMedian OS: ", round(Cirrhosis_median_OS_NO, 2), " months\n",
"95% CI: ", round(Cirrhosis_lower_OS_ci_NO, 2), "-", round(Cirrhosis_upper_OS_ci_NO, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D") +
geom_label(aes(x = 37, y = 0.90,
label = paste("YES \nMedian OS: ", round(Cirrhosis_median_OS_YES, 2), " months\n",
"95% CI: ", round(Cirrhosis_lower_OS_ci_YES, 2), "-", round(Cirrhosis_upper_OS_ci_YES, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4")## Error: object 'Cirrhosis_OS_plot' not found## Error: object 'Cirrhosis_OS_plot' not found# High-resolution plot saving
png("Cirrhosis_OS_TKIs.png", width = 8, height = 6, units = "in", res = 300)
print(Cirrhosis_OS_plot)## Error: object 'Cirrhosis_OS_plot' not found## quartz_off_screen
## 2#### Cirrhosis Analysis
###For Cirrhosis survival curve the right hand side should be ~ Cirrhosis
Cirrhosis_PFS = survfit(Surv(Time_PFS, PFS_Status) ~ Cirrhosis , data = ClinicData)
summary_data_Cirrhosis_PFS <- summary(Cirrhosis_PFS)
# Extract median PFS and confidence intervals for each group
Cirrhosis_median_PFS_NO <- summary_data_Cirrhosis_PFS$table["Cirrhosis=NO", "median"]
Cirrhosis_lower_PFS_ci_NO <- summary_data_Cirrhosis_PFS$table["Cirrhosis=NO", "0.95LCL"]
Cirrhosis_upper_PFS_ci_NO <- summary_data_Cirrhosis_PFS$table["Cirrhosis=NO", "0.95UCL"]
Cirrhosis_median_PFS_YES <- summary_data_Cirrhosis_PFS$table["Cirrhosis=YES", "median"]
Cirrhosis_lower_PFS_ci_YES <- summary_data_Cirrhosis_PFS$table["Cirrhosis=YES", "0.95LCL"]
Cirrhosis_upper_PFS_ci_YES <- summary_data_Cirrhosis_PFS$table["Cirrhosis=YES", "0.95UCL"]
Cirrhosis_PFS_plot <- ggsurvplot(Cirrhosis_PFS,
conf.int=FALSE, # add confidence intervals
pval=TRUE, # show the p-value for the log-rank test
pval.method = TRUE, #corresponds to survival curves' comparison
pval.size= 4,# numeric value specifying the p-value text size. Default is 5.
test.for.trend = FALSE,
risk.table=TRUE,# show a risk table below the plot.
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", #for drawing a horizontal/vertical line at median survival.
legend.labs=c("No", "Yes"), # change group labels
tables.col = "strata",
tables.y.text.col=TRUE,
fontsize=5,
legend.title="Cirrhosis", # add legend title
risk.table.title="Number at risk", #the title to be used for the risk table.
title=NULL, # add title to plot
risk.table.height=.25,
add.all = FALSE,
combine = FALSE,
xlab="Time, months",
ylab="PFS Probability",
axes.offset=TRUE,
surv.plot.height=1,
font.x = 15,
font.y = 15,
font.legend.title=15)## Error in ggsurvplot_df(d, fun = fun, color = color, palette = palette, : The length of legend.labs should be 3# Annotate the median PFS and confidence interval for group
Cirrhosis_PFS_plot$plot <- Cirrhosis_PFS_plot$plot +
geom_label(aes(x = 25, y = 0.60,
label = paste("NO \nMedian PFS: ", round(Cirrhosis_median_PFS_NO, 2), " months\n",
"95% CI: ", round(Cirrhosis_lower_PFS_ci_NO, 2), "-", round(Cirrhosis_upper_PFS_ci_NO, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D") +
geom_label(aes(x = 25, y = 0.90,
label = paste("YES \nMedian PFS: ", round(Cirrhosis_median_PFS_YES, 2), " months\n",
"95% CI: ", round(Cirrhosis_lower_PFS_ci_YES, 2), "-", round(Cirrhosis_upper_PFS_ci_YES, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4")## Error: object 'Cirrhosis_PFS_plot' not found## Error: object 'Cirrhosis_PFS_plot' not found# High-resolution plot saving
png("Cirrhosis_PFS_TKIs.png", width = 8, height = 6, units = "in", res = 300)
print(Cirrhosis_PFS_plot)## Error: object 'Cirrhosis_PFS_plot' not found## quartz_off_screen
## 2# Create binary variable for Tumour Size
TumourSize_binary <- ifelse(ClinicData$Tumour.largest.size..cm. < 5, "low", "high")
ClinicData$TumourSize_binary <- TumourSize_binary
# Fit the survival model for OS
TumourSize_OS <- survfit(Surv(Time_OS, OS_Status) ~ TumourSize_binary, data = ClinicData)
summary_data_TumourSize_OS <- summary(TumourSize_OS)
# Extract median OS and confidence intervals for high group
TumourSize_median_OS_high <- summary_data_TumourSize_OS$table["TumourSize_binary=high", "median"]
TumourSize_lower_OS_ci_high <- summary_data_TumourSize_OS$table["TumourSize_binary=high", "0.95LCL"]
TumourSize_upper_OS_ci_high <- summary_data_TumourSize_OS$table["TumourSize_binary=high", "0.95UCL"]
# Extract median OS and confidence intervals for low group
TumourSize_median_OS_low <- summary_data_TumourSize_OS$table["TumourSize_binary=low", "median"]
TumourSize_lower_OS_ci_low <- summary_data_TumourSize_OS$table["TumourSize_binary=low", "0.95LCL"]
TumourSize_upper_OS_ci_low <- summary_data_TumourSize_OS$table["TumourSize_binary=low", "0.95UCL"]
# Create the survival plot
TumourSize_OS_plot <- ggsurvplot(TumourSize_OS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(40, 0.25), # <- move the "log-rank" method label here
pval.coord = c(40, 0.20), # <- move the log-rank p-value here (x, y)
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("Tumour size ≥ 5", "Tumour size < 5 cm"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "OS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median OS and confidence interval for each group
TumourSize_OS_plot$plot <- TumourSize_OS_plot$plot +
geom_label(aes(x = 35, y = 0.45,
label = paste("Tumour size < 5 cm\nMedian OS:", round(TumourSize_median_OS_low, 2), "months\n",
"95% CI:", round(TumourSize_lower_OS_ci_low, 2), "-", round(TumourSize_upper_OS_ci_low, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 35, y = 0.75,
label = paste("Tumour size ≥ 5 cm\nMedian OS:", round(TumourSize_median_OS_high, 2), "months\n",
"95% CI:", round(TumourSize_lower_OS_ci_high, 2), "-", round(TumourSize_upper_OS_ci_high, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")
# Print the plot in the R Markdown document
print(TumourSize_OS_plot)
# High-resolution plot saving
png("TumourSize_OS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(TumourSize_OS_plot)
dev.off()## quartz_off_screen
## 2# Create binary variable for Tumour Size
TumourSize_binary <- ifelse(ClinicData$Tumour.largest.size..cm. < 5, "low", "high")
ClinicData$TumourSize_binary <- TumourSize_binary
# Fit the survival model for PFS
TumourSize_PFS <- survfit(Surv(Time_PFS, PFS_Status) ~ TumourSize_binary, data = ClinicData)
summary_data_TumourSize_PFS <- summary(TumourSize_PFS)
# Extract median PFS and confidence intervals for high group
TumourSize_median_PFS_high <- summary_data_TumourSize_PFS$table["TumourSize_binary=high", "median"]
TumourSize_lower_PFS_ci_high <- summary_data_TumourSize_PFS$table["TumourSize_binary=high", "0.95LCL"]
TumourSize_upper_PFS_ci_high <- summary_data_TumourSize_PFS$table["TumourSize_binary=high", "0.95UCL"]
# Extract median PFS and confidence intervals for low group
TumourSize_median_PFS_low <- summary_data_TumourSize_PFS$table["TumourSize_binary=low", "median"]
TumourSize_lower_PFS_ci_low <- summary_data_TumourSize_PFS$table["TumourSize_binary=low", "0.95LCL"]
TumourSize_upper_PFS_ci_low <- summary_data_TumourSize_PFS$table["TumourSize_binary=low", "0.95UCL"]
# Create the survival plot
TumourSize_PFS_plot <- ggsurvplot(TumourSize_PFS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(30, 0.25), # <- move the "log-rank" method label here
pval.coord = c(30, 0.20), # <- move the log-rank p-value here (x, y)
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("Tumour size ≥ 5", "Tumour size < 5 cm"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "PFS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median PFS and confidence interval for each group
TumourSize_PFS_plot$plot <- TumourSize_PFS_plot$plot +
geom_label(aes(x = 25, y = 0.45,
label = paste("Tumour size < 5 cm\nMedian PFS:", round(TumourSize_median_PFS_low, 2), "months\n",
"95% CI:", round(TumourSize_lower_PFS_ci_low, 2), "-", round(TumourSize_upper_PFS_ci_low, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 25, y = 0.75,
label = paste("Tumour size ≥ 5 cm\nMedian PFS:", round(TumourSize_median_PFS_high, 2), "months\n",
"95% CI:", round(TumourSize_lower_PFS_ci_high, 2), "-", round(TumourSize_upper_PFS_ci_high, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")
# Print the plot in the R Markdown document
print(TumourSize_PFS_plot)
# High-resolution plot saving
png("TumourSize_PFS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(TumourSize_PFS_plot)
dev.off()## quartz_off_screen
## 2# Create binary variable for Tumour Size
tMAD_binary <- ifelse(ClinicData$tMAD.Score <= 0.02, "low", "high")
ClinicData$tMAD_binary <- tMAD_binary## Error in `$<-.data.frame`(`*tmp*`, tMAD_binary, value = logical(0)): replacement has 0 rows, data has 134# Fit the survival model for OS
tMAD_OS <- survfit(Surv(Time_OS, OS_Status) ~ tMAD_binary, data = ClinicData, conf.type = "log-log")## Error in model.frame.default(formula = Surv(Time_OS, OS_Status) ~ tMAD_binary, : variable lengths differ (found for 'tMAD_binary')## Error: object 'tMAD_OS' not found# Extract median OS and confidence intervals for high group
tMAD_median_OS_high <- summary_data_tMAD_OS$table["tMAD_binary=high", "median"]## Error: object 'summary_data_tMAD_OS' not found## Error: object 'summary_data_tMAD_OS' not found## Error: object 'summary_data_tMAD_OS' not found# Extract median OS and confidence intervals for low group
tMAD_median_OS_low <- summary_data_tMAD_OS$table["tMAD_binary=low", "median"]## Error: object 'summary_data_tMAD_OS' not found## Error: object 'summary_data_tMAD_OS' not found## Error: object 'summary_data_tMAD_OS' not found# Create the survival plot
tMAD_OS_plot <- ggsurvplot(tMAD_OS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(5, 0.30), # <- move the "log-rank" method label here
pval.coord = c(5, 0.25),
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = NULL, # horizontal/vertical line at median survival
legend.labs = c("tMAD > 0.02", "tMAD ≤ 0.02"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "OS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)## Error: object 'tMAD_OS' not found# Annotate the median OS and confidence interval for each group
tMAD_OS_plot$plot <- tMAD_OS_plot$plot +
geom_label(aes(x = 35, y = 0.45,
label = paste("tMAD ≤ 0.02 \nMedian OS:", round(tMAD_median_OS_low, 2), "months\n",
"95% CI:", round(tMAD_lower_OS_ci_low, 2), "-", round(tMAD_upper_OS_ci_low, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 35, y = 0.75,
label = paste("tMAD > 0.02 \nMedian OS:", round(tMAD_median_OS_high, 2), "months\n",
"95% CI:", round(tMAD_lower_OS_ci_high, 2), "-", round(tMAD_upper_OS_ci_high, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")## Error: object 'tMAD_OS_plot' not found## Error: object 'tMAD_OS_plot' not found# High-resolution plot saving
png("tMAD_OS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(tMAD_OS_plot)## Error: object 'tMAD_OS_plot' not found## quartz_off_screen
## 2# Create binary variable for Tumour Size
tMAD_binary <- ifelse(ClinicData$tMAD.Score <= 0.02, "low", "high")
ClinicData$tMAD_binary <- tMAD_binary## Error in `$<-.data.frame`(`*tmp*`, tMAD_binary, value = logical(0)): replacement has 0 rows, data has 134# Fit the survival model for PFS
tMAD_PFS <- survfit(Surv(Time_PFS, PFS_Status) ~ tMAD_binary, data = ClinicData, conf.type = "log-log")## Error in model.frame.default(formula = Surv(Time_PFS, PFS_Status) ~ tMAD_binary, : variable lengths differ (found for 'tMAD_binary')## Error: object 'tMAD_PFS' not found# Extract median PFS and confidence intervals for high group
tMAD_median_PFS_high <- summary_data_tMAD_PFS$table["tMAD_binary=high", "median"]## Error: object 'summary_data_tMAD_PFS' not found## Error: object 'summary_data_tMAD_PFS' not found## Error: object 'summary_data_tMAD_PFS' not found# Extract median PFS and confidence intervals for low group
tMAD_median_PFS_low <- summary_data_tMAD_PFS$table["tMAD_binary=low", "median"]## Error: object 'summary_data_tMAD_PFS' not found## Error: object 'summary_data_tMAD_PFS' not found## Error: object 'summary_data_tMAD_PFS' not found# Create the survival plot
tMAD_PFS_plot <- ggsurvplot(tMAD_PFS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(30, 0.25), # <- move the "log-rank" method label here
pval.coord = c(30, 0.20),
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = NULL, # horizontal/vertical line at median survival
legend.labs = c("tMAD > 0.02", "tMAD ≤ 0.02"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "PFS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)## Error: object 'tMAD_PFS' not found# Annotate the median PFS and confidence interval for each group
tMAD_PFS_plot$plot <- tMAD_PFS_plot$plot +
geom_label(aes(x = 25, y = 0.45,
label = paste("tMAD ≤ 0.02 \nMedian PFS:", round(tMAD_median_PFS_low, 2), "months\n",
"95% CI:", round(tMAD_lower_PFS_ci_low, 2), "-", round(tMAD_upper_PFS_ci_low, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 25, y = 0.75,
label = paste("tMAD > 0.02 \nMedian PFS:", round(tMAD_median_PFS_high, 2), "months\n",
"95% CI:", round(tMAD_lower_PFS_ci_high, 2), "-", round(tMAD_upper_PFS_ci_high, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")## Error: object 'tMAD_PFS_plot' not found## Error: object 'tMAD_PFS_plot' not found# High-resolution plot saving
png("tMAD_PFS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(tMAD_PFS_plot)## Error: object 'tMAD_PFS_plot' not found## quartz_off_screen
## 2# Create binary variable for Tumour Size
ClinicData <- read.csv("/Volumes/Samsung_SSD_8TB/Chapter 2 (Prognostic Indicators for HCC)/ICI_TKI_Ram_Sul_Mat_2025-01-29_Final.csv")
DELFI_binary <- ifelse(ClinicData$DELFI_Baseline <= 0.287, "low", "high")
ClinicData$DELFI_Baseline <- DELFI_binary
# Fit the survival model for OS
DELFI_OS <- survfit(Surv(Time_OS, OS_Status) ~ DELFI_binary, data = ClinicData, conf.type = "log-log")
summary_data_DELFI_OS <- summary(DELFI_OS)
# Extract median OS and confidence intervals for high group
DELFI_median_OS_high <- summary_data_DELFI_OS$table["DELFI_binary=high", "median"]
DELFI_lower_OS_ci_high <- summary_data_DELFI_OS$table["DELFI_binary=high", "0.95LCL"]
DELFI_upper_OS_ci_high <- summary_data_DELFI_OS$table["DELFI_binary=high", "0.95UCL"]
# Extract median OS and confidence intervals for low group
DELFI_median_OS_low <- summary_data_DELFI_OS$table["DELFI_binary=low", "median"]
DELFI_lower_OS_ci_low <- summary_data_DELFI_OS$table["DELFI_binary=low", "0.95LCL"]
DELFI_upper_OS_ci_low <- summary_data_DELFI_OS$table["DELFI_binary=low", "0.95UCL"]
# Create the survival plot
DELFI_OS_plot <- ggsurvplot(DELFI_OS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(40, 0.30), # <- move the "log-rank" method label here
pval.coord = c(40, 0.25), # <- move the log-rank p-value here (x, y)
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("DELFI > 0.4", "DELFI ≤ 0.4"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "OS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median OS and confidence interval for each group
DELFI_OS_plot$plot <- DELFI_OS_plot$plot +
geom_label(aes(x = 35, y = 0.45,
label = paste("DELFI ≤ 0.3 \nMedian OS:", round(DELFI_median_OS_low, 2), "months\n",
"95% CI:", round(DELFI_lower_OS_ci_low, 2), "-", round(DELFI_upper_OS_ci_low, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 35, y = 0.75,
label = paste("DELFI > 0.3 \nMedian OS:", round(DELFI_median_OS_high, 2), "months\n",
"95% CI:", round(DELFI_lower_OS_ci_high, 2), "-", round(DELFI_upper_OS_ci_high, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")
# Print the plot in the R Markdown document
print(DELFI_OS_plot)
# High-resolution plot saving
png("DELFI_OS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(DELFI_OS_plot)
dev.off()## quartz_off_screen
## 2# Create binary variable for Tumour Size
ClinicData <- read.csv("/Volumes/Samsung_SSD_8TB/Chapter 2 (Prognostic Indicators for HCC)/ICI_TKI_Ram_Sul_Mat_2025-01-29_Final.csv")
DELFI_binary <- ifelse(ClinicData$DELFI_Baseline <= 0.287, "low", "high")
ClinicData$DELFI_Baseline <- DELFI_binary
# Fit the survival model for PFS
DELFI_PFS <- survfit(Surv(Time_PFS, PFS_Status) ~ DELFI_binary, data = ClinicData, conf.type = "log-log")
summary_data_DELFI_PFS <- summary(DELFI_PFS)
# Extract median PFS and confidence intervals for high group
DELFI_median_PFS_high <- summary_data_DELFI_PFS$table["DELFI_binary=high", "median"]
DELFI_lower_PFS_ci_high <- summary_data_DELFI_PFS$table["DELFI_binary=high", "0.95LCL"]
DELFI_upper_PFS_ci_high <- summary_data_DELFI_PFS$table["DELFI_binary=high", "0.95UCL"]
# Extract median PFS and confidence intervals for low group
DELFI_median_PFS_low <- summary_data_DELFI_PFS$table["DELFI_binary=low", "median"]
DELFI_lower_PFS_ci_low <- summary_data_DELFI_PFS$table["DELFI_binary=low", "0.95LCL"]
DELFI_upper_PFS_ci_low <- summary_data_DELFI_PFS$table["DELFI_binary=low", "0.95UCL"]
# Create the survival plot
DELFI_PFS_plot <- ggsurvplot(DELFI_PFS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(30, 0.25), # <- move the "log-rank" method label here
pval.coord = c(30, 0.20), # <- move the log-rank p-value here (x, y)
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("High DELFI", "Low DELFI"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "PFS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median PFS and confidence interval for each group
DELFI_PFS_plot$plot <- DELFI_PFS_plot$plot +
geom_label(aes(x = 25, y = 0.45,
label = paste("Low DELFI \nMedian PFS:", round(DELFI_median_PFS_low, 2), "months\n",
"95% CI:", round(DELFI_lower_PFS_ci_low, 2), "-", round(DELFI_upper_PFS_ci_low, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 25, y = 0.75,
label = paste("High DELFI \nMedian PFS:", round(DELFI_median_PFS_high, 2), "months\n",
"95% CI:", round(DELFI_lower_PFS_ci_high, 2), "-", round(DELFI_upper_PFS_ci_high, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")
# Print the plot in the R Markdown document
print(DELFI_PFS_plot)
# High-resolution plot saving
png("DELFI_PFS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(DELFI_PFS_plot)
dev.off()## quartz_off_screen
## 2# Fit the survival model for OS
Responsce_OS = survfit(Surv(Time_OS, OS_Status) ~Best_Responsce , data = ClinicData, conf.type = "log-log")
summary_data_Responsce_OS <- summary(Responsce_OS)
# Extract median OS and confidence intervals for high group
Responsce_median_OS_PD <- summary_data_Responsce_OS$table["Best_Responsce=PD", "median"]
Responsce_lower_OS_ci_PD <- summary_data_Responsce_OS$table["Best_Responsce=PD", "0.95LCL"]
Responsce_upper_OS_ci_PD <- summary_data_Responsce_OS$table["Best_Responsce=PD", "0.95UCL"]
# Extract median OS and confidence intervals for low group
Responsce_median_OS_SD <- summary_data_Responsce_OS$table["Best_Responsce=SD", "median"]
Responsce_lower_OS_ci_SD <- summary_data_Responsce_OS$table["Best_Responsce=SD", "0.95LCL"]
Responsce_upper_OS_ci_SD <- summary_data_Responsce_OS$table["Best_Responsce=SD", "0.95UCL"]
# Create the survival plot
Responsce_OS_plot <- ggsurvplot(Responsce_OS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(45, 0.25), # <- move the "log-rank" method label here
pval.coord = c(45, 0.18), # <- move the log-rank p-value here (x, y)
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("PD", "SD"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "Responsce", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "OS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median OS and confidence interval for each group
Responsce_OS_plot$plot <- Responsce_OS_plot$plot +
geom_label(aes(x = 40, y = 0.45,
label = paste("PD\nMedian OS:", round(Responsce_median_OS_PD, 2), "months\n",
"95% CI:", round(Responsce_lower_OS_ci_PD, 2), "-", round(Responsce_upper_OS_ci_PD, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D") +
geom_label(aes(x = 40, y = 0.75,
label = paste("SD\nMedian OS:", round(Responsce_median_OS_SD, 2), "months\n",
"95% CI:", round(Responsce_lower_OS_ci_SD, 2), "-", round(Responsce_upper_OS_ci_SD, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4")
# Print the plot in the R Markdown document
print(Responsce_OS_plot)
# High-resolution plot saving
png("Responsce_OS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(Responsce_OS_plot)
dev.off()## quartz_off_screen
## 2# Fit the survival model for PFS
Responsce_PFS = survfit(Surv(Time_PFS, PFS_Status) ~Best_Responsce , data = ClinicData, conf.type = "log-log")
summary_data_Responsce_PFS <- summary(Responsce_PFS)
# Extract median PFS and confidence intervals for high group
Responsce_median_PFS_PD <- summary_data_Responsce_PFS$table["Best_Responsce=PD", "median"]
Responsce_lower_PFS_ci_PD <- summary_data_Responsce_PFS$table["Best_Responsce=PD", "0.95LCL"]
Responsce_upper_PFS_ci_PD <- summary_data_Responsce_PFS$table["Best_Responsce=PD", "0.95UCL"]
# Extract median PFS and confidence intervals for low group
Responsce_median_PFS_SD <- summary_data_Responsce_PFS$table["Best_Responsce=SD", "median"]
Responsce_lower_PFS_ci_SD <- summary_data_Responsce_PFS$table["Best_Responsce=SD", "0.95LCL"]
Responsce_upper_PFS_ci_SD <- summary_data_Responsce_PFS$table["Best_Responsce=SD", "0.95UCL"]
# Create the survival plot
Responsce_PFS_plot <- ggsurvplot(Responsce_PFS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(30, 0.20), # <- move the "log-rank" method label here
pval.coord = c(30, 0.15), # <- move the log-rank p-value here (x, y)
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("PD", "SD"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "Responsce", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "PFS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median PFS and confidence interval for each group
Responsce_PFS_plot$plot <- Responsce_PFS_plot$plot +
geom_label(aes(x = 25, y = 0.45,
label = paste("PD\nMedian PFS:", round(Responsce_median_PFS_PD, 2), "months\n",
"95% CI:", round(Responsce_lower_PFS_ci_PD, 2), "-", round(Responsce_upper_PFS_ci_PD, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D") +
geom_label(aes(x = 25, y = 0.75,
label = paste("SD\nMedian PFS:", round(Responsce_median_PFS_SD, 2), "months\n",
"95% CI:", round(Responsce_lower_PFS_ci_SD, 2), "-", round(Responsce_upper_PFS_ci_SD, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4")
# Print the plot in the R Markdown document
print(Responsce_PFS_plot)
# High-resolution plot saving
png("Responsce_PFS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(Responsce_PFS_plot)
dev.off()## quartz_off_screen
## 2# Create binary variable for AFP
AFP_binary <- ifelse(ClinicData$AFP <= 400, "low", "high")
ClinicData$AFP_binary <- AFP_binary
# Fit the survival model for OS
AFP_OS <- survfit(Surv(Time_OS, OS_Status) ~ AFP_binary, data = ClinicData, conf.type = "log-log")
summary_data_AFP_OS <- summary(AFP_OS)
# Extract median OS and confidence intervals for high group
AFP_median_OS_high <- summary_data_AFP_OS$table["AFP_binary=high", "median"]
AFP_lower_OS_ci_high <- summary_data_AFP_OS$table["AFP_binary=high", "0.95LCL"]
AFP_upper_OS_ci_high <- summary_data_AFP_OS$table["AFP_binary=high", "0.95UCL"]
# Extract median OS and confidence intervals for low group
AFP_median_OS_low <- summary_data_AFP_OS$table["AFP_binary=low", "median"]
AFP_lower_OS_ci_low <- summary_data_AFP_OS$table["AFP_binary=low", "0.95LCL"]
AFP_upper_OS_ci_low <- summary_data_AFP_OS$table["AFP_binary=low", "0.95UCL"]
# Create the survival plot
AFP_OS_plot <- ggsurvplot(AFP_OS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(50, 0.25), # <- move the "log-rank" method label here
pval.coord = c(50, 0.18),
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("AFP ≥ 400", "AFP < 400"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "OS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median OS and confidence interval for each group
AFP_OS_plot$plot <- AFP_OS_plot$plot +
geom_label(aes(x = 40, y = 0.55,
label = paste("AFP < 400\nMedian OS:", round(AFP_median_OS_low, 2), "months\n",
"95% CI:", round(AFP_lower_OS_ci_low, 2), "-", round(AFP_upper_OS_ci_low, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 40, y = 0.85,
label = paste("AFP ≥ 400\nMedian OS:", round(AFP_median_OS_high, 2), "months\n",
"95% CI:", round(AFP_lower_OS_ci_high, 2), "-", round(AFP_upper_OS_ci_high, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")
# Print the plot in the R Markdown document
print(AFP_OS_plot)
# High-resolution plot saving
png("AFP_OS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(AFP_OS_plot)
dev.off()## quartz_off_screen
## 2# Create binary variable for AFP
AFP_binary <- ifelse(ClinicData$AFP <= 400, "low", "high")
ClinicData$AFP_binary <- AFP_binary
# Fit the survival model for PFS
AFP_PFS <- survfit(Surv(Time_PFS, PFS_Status) ~ AFP_binary, data = ClinicData, conf.type = "log-log")
summary_data_AFP_PFS <- summary(AFP_PFS)
# Extract median PFS and confidence intervals for high group
AFP_median_PFS_high <- summary_data_AFP_PFS$table["AFP_binary=high", "median"]
AFP_lower_PFS_ci_high <- summary_data_AFP_PFS$table["AFP_binary=high", "0.95LCL"]
AFP_upper_PFS_ci_high <- summary_data_AFP_PFS$table["AFP_binary=high", "0.95UCL"]
# Extract median PFS and confidence intervals for low group
AFP_median_PFS_low <- summary_data_AFP_PFS$table["AFP_binary=low", "median"]
AFP_lower_PFS_ci_low <- summary_data_AFP_PFS$table["AFP_binary=low", "0.95LCL"]
AFP_upper_PFS_ci_low <- summary_data_AFP_PFS$table["AFP_binary=low", "0.95UCL"]
# Create the survival plot
AFP_PFS_plot <- ggsurvplot(AFP_PFS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(35, 0.25), # <- move the "log-rank" method label here
pval.coord = c(35, 0.18),
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("AFP ≥ 400", "AFP < 400"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "PFS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median PFS and confidence interval for each group
AFP_PFS_plot$plot <- AFP_PFS_plot$plot +
geom_label(aes(x = 25, y = 0.45,
label = paste("AFP < 400\nMedian PFS:", round(AFP_median_PFS_low, 2), "months\n",
"95% CI:", round(AFP_lower_PFS_ci_low, 2), "-", round(AFP_upper_PFS_ci_low, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 25, y = 0.75,
label = paste("AFP ≥ 400\nMedian PFS:", round(AFP_median_PFS_high, 2), "months\n",
"95% CI:", round(AFP_lower_PFS_ci_high, 2), "-", round(AFP_upper_PFS_ci_high, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")
# Print the plot in the R Markdown document
print(AFP_PFS_plot)
# High-resolution plot saving
png("AFP_PFS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(AFP_PFS_plot)
dev.off()## quartz_off_screen
## 2# Create binary variable for Age
Age_binary <- ifelse(ClinicData$Age <= 70, "low", "high")
ClinicData$Age_binary <- Age_binary
# Fit the survival model for OS
Age_OS <- survfit(Surv(Time_OS, OS_Status) ~ Age_binary, data = ClinicData, conf.type = "log-log")
summary_data_Age_OS <- summary(Age_OS)
# Extract median OS and confidence intervals for high group
Age_median_OS_high <- summary_data_Age_OS$table["Age_binary=high", "median"]
Age_lower_OS_ci_high <- summary_data_Age_OS$table["Age_binary=high", "0.95LCL"]
Age_upper_OS_ci_high <- summary_data_Age_OS$table["Age_binary=high", "0.95UCL"]
# Extract median OS and confidence intervals for low group
Age_median_OS_low <- summary_data_Age_OS$table["Age_binary=low", "median"]
Age_lower_OS_ci_low <- summary_data_Age_OS$table["Age_binary=low", "0.95LCL"]
Age_upper_OS_ci_low <- summary_data_Age_OS$table["Age_binary=low", "0.95UCL"]
# Create the survival plot
Age_OS_plot <- ggsurvplot(Age_OS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(45, 0.25), # <- move the "log-rank" method label here
pval.coord = c(45, 0.20),
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("Age > 70", "Age ≤ 70"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "OS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median OS and confidence interval for each group
Age_OS_plot$plot <- Age_OS_plot$plot +
geom_label(aes(x = 35, y = 0.45,
label = paste("Age ≤ 70 \nMedian OS:", round(Age_median_OS_low, 2), "months\n",
"95% CI:", round(Age_lower_OS_ci_low, 2), "-", round(Age_upper_OS_ci_low, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 35, y = 0.75,
label = paste("Age > 70 \nMedian OS:", round(Age_median_OS_high, 2), "months\n",
"95% CI:", round(Age_lower_OS_ci_high, 2), "-", round(Age_upper_OS_ci_high, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")
# Print the plot in the R Markdown document
print(Age_OS_plot)
# High-resolution plot saving
png("Age_OS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(Age_OS_plot)
dev.off()## quartz_off_screen
## 2# Create binary variable for Age
Age_binary <- ifelse(ClinicData$Age <= 70, "low", "high")
ClinicData$Age_binary <- Age_binary
# Fit the survival model for PFS
Age_PFS <- survfit(Surv(Time_PFS, PFS_Status) ~ Age_binary, data = ClinicData, conf.type = "log-log")
summary_data_Age_PFS <- summary(Age_PFS)
# Extract median PFS and confidence intervals for high group
Age_median_PFS_high <- summary_data_Age_PFS$table["Age_binary=high", "median"]
Age_lower_PFS_ci_high <- summary_data_Age_PFS$table["Age_binary=high", "0.95LCL"]
Age_upper_PFS_ci_high <- summary_data_Age_PFS$table["Age_binary=high", "0.95UCL"]
# Extract median PFS and confidence intervals for low group
Age_median_PFS_low <- summary_data_Age_PFS$table["Age_binary=low", "median"]
Age_lower_PFS_ci_low <- summary_data_Age_PFS$table["Age_binary=low", "0.95LCL"]
Age_upper_PFS_ci_low <- summary_data_Age_PFS$table["Age_binary=low", "0.95UCL"]
# Create the survival plot
Age_PFS_plot <- ggsurvplot(Age_PFS,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(35, 0.20), # <- move the "log-rank" method label here
pval.coord = c(35, 0.15),
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("Age > 70", "Age ≤ 70"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "PFS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median PFS and confidence interval for each group
Age_PFS_plot$plot <- Age_PFS_plot$plot +
geom_label(aes(x = 25, y = 0.45,
label = paste("Age ≤ 70 \nMedian PFS:", round(Age_median_PFS_low, 2), "months\n",
"95% CI:", round(Age_lower_PFS_ci_low, 2), "-", round(Age_upper_PFS_ci_low, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 25, y = 0.75,
label = paste("Age > 70 \nMedian PFS:", round(Age_median_PFS_high, 2), "months\n",
"95% CI:", round(Age_lower_PFS_ci_high, 2), "-", round(Age_upper_PFS_ci_high, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")
# Print the plot in the R Markdown document
print(Age_PFS_plot)
# High-resolution plot saving
png("Age_PFS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(Age_PFS_plot)
dev.off()## quartz_off_screen
## 2# Ensure consistent strata order
ClinicData$Treatment.Class <- factor(ClinicData$Treatment.Class,
levels = c("TKI","ICI"))## Error in `$<-.data.frame`(`*tmp*`, Treatment.Class, value = structure(integer(0), levels = c("TKI", : replacement has 0 rows, data has 134# Fit
fit <- survfit(Surv(Time_OS, OS_Status) ~ Treatment.Class,
data = ClinicData, conf.type = "log-log")## Error in eval(predvars, data, env): object 'Treatment.Class' not found## Error: object 'fit' not found## Error: object 'med' not found## Error: object 'med' not found# Named palette (GGplot defaults shown; change if you prefer)
pal <- c(TKI = "#F8766D", ICI = "#00BFC4")
plt <- ggsurvplot(
fit,
palette = pal, # <- named mapping
legend.labs = c("TKI","ICI"),
legend.title = "Treatment",
conf.int = FALSE,
pval = TRUE, pval.method = TRUE,
pval.method.coord = c(40, 0.30), # <- move the "log-rank" method label here
pval.coord = c(40, 0.20), # <- move the log-rank p-value here (x, y)
risk.table = TRUE, tables.col = "strata", tables.y.text.col = TRUE,
surv.median.line = "hv",
xlab = "Time, months", ylab = "OS Probability",
risk.table.height = 0.25, surv.plot.height = 1,
font.x = 15, font.y = 15, font.legend.title = 15, fontsize = 5
)## Error: object 'fit' not found# Use the same palette for annotation fills
plt$plot <- plt$plot +
annotate("label", x = 37, y = 0.60,
label = sprintf("TKI\nMedian OS: %.2f months\n95%% CI: %.2f–%.2f",
tki$median, tki$lower, tki$upper),
size = 4, hjust = 0, vjust = 1, color = "black",
fill = pal["TKI"]) +
annotate("label", x = 37, y = 0.90,
label = sprintf("ICI\nMedian OS: %.2f months\n95%% CI: %.2f–%.2f",
ici$median, ici$lower, ici$upper),
size = 4, hjust = 0, vjust = 1, color = "black",
fill = pal["ICI"])## Error: object 'plt' not found# Save high-resolution Kaplan–Meier OS plot
png("Treatment_Type_OS_TKI_ICI.png",
width = 8, height = 6, units = "in", res = 500) # You can use 300 or 600 dpi
print(plt)## Error: object 'plt' not found## quartz_off_screen
## 2# Ensure consistent strata order
ClinicData$Treatment.Class <- factor(ClinicData$Treatment.Class,
levels = c("TKI","ICI"))## Error in `$<-.data.frame`(`*tmp*`, Treatment.Class, value = structure(integer(0), levels = c("TKI", : replacement has 0 rows, data has 134# Fit
fit <- survfit(Surv(Time_PFS, PFS_Status) ~ Treatment.Class,
data = ClinicData, conf.type = "log-log")## Error in eval(predvars, data, env): object 'Treatment.Class' not found## Error: object 'fit' not found## Error: object 'med' not found## Error: object 'med' not found# Named palette (GGplot defaults shown; change if you prefer)
pal <- c(TKI = "#F8766D", ICI = "#00BFC4")
plt <- ggsurvplot(
fit,
palette = pal, # <- named mapping
legend.labs = c("TKI","ICI"),
legend.title = "Treatment",
conf.int = FALSE,
pval = TRUE, pval.method = TRUE,
pval.method.coord = c(40, 0.30), # <- move the "log-rank" method label here
pval.coord = c(40, 0.20), # <- move the log-rank p-value here (x, y)
risk.table = TRUE, tables.col = "strata", tables.y.text.col = TRUE,
surv.median.line = "hv",
xlab = "Time, months", ylab = "PFS Probability",
risk.table.height = 0.25, surv.plot.height = 1,
font.x = 15, font.y = 15, font.legend.title = 15, fontsize = 5
)## Error: object 'fit' not found# Use the same palette for annotation fills
plt$plot <- plt$plot +
annotate("label", x = 37, y = 0.60,
label = sprintf("TKI\nMedian PFS: %.2f months\n95%% CI: %.2f–%.2f",
tki$median, tki$lower, tki$upper),
size = 4, hjust = 0, vjust = 1, color = "black",
fill = pal["TKI"]) +
annotate("label", x = 37, y = 0.90,
label = sprintf("ICI\nMedian PFS: %.2f months\n95%% CI: %.2f–%.2f",
ici$median, ici$lower, ici$upper),
size = 4, hjust = 0, vjust = 1, color = "black",
fill = pal["ICI"])## Error: object 'plt' not found# Save high-resolution Kaplan–Meier OS plot
png("Treatment_Type_PFS_TKI_ICI.png",
width = 8, height = 6, units = "in", res = 500) # You can use 300 or 600 dpi
print(plt)## Error: object 'plt' not found## quartz_off_screen
## 2#### PVT_EHS Analysis
###For PVT_EHS survival curve the right hand side should be ~ PVT_EHS
PVT_EHS_OS = survfit(Surv(Time_OS, OS_Status) ~ PVT , data = ClinicData)
summary_data_PVT_EHS_OS <- summary(PVT_EHS_OS)
# Extract median OS and confidence intervals for each group
PVT_EHS_median_OS_NO <- summary_data_PVT_EHS_OS$table["PVT=No", "median"]
PVT_EHS_lower_OS_ci_NO <- summary_data_PVT_EHS_OS$table["PVT=No", "0.95LCL"]
PVT_EHS_upper_OS_ci_NO <- summary_data_PVT_EHS_OS$table["PVT=No", "0.95UCL"]
PVT_EHS_median_OS_YES <- summary_data_PVT_EHS_OS$table["PVT=Yes", "median"]
PVT_EHS_lower_OS_ci_YES <- summary_data_PVT_EHS_OS$table["PVT=Yes", "0.95LCL"]
PVT_EHS_upper_OS_ci_YES <- summary_data_PVT_EHS_OS$table["PVT=Yes", "0.95UCL"]
PVT_EHS_OS_plot <- ggsurvplot(PVT_EHS_OS,
conf.int=FALSE, # add confidence intervals
pval=TRUE, # show the p-value for the log-rank test
pval.method = TRUE, #corresponds to survival curves' comparison
pval.size= 4,# numeric value specifying the p-value text size. Default is 5.
pval.method.coord = c(45, 0.30), # <- move the "log-rank" method label here
pval.coord = c(45, 0.25),
test.for.trend = FALSE,
risk.table=TRUE,# show a risk table below the plot.
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", #for drawing a horizontal/vertical line at median survival.
legend.labs=c("Absent", "Present"), # change group labels
tables.col = "strata",
tables.y.text.col=TRUE,
fontsize=5,
legend.title="PVT", # add legend title
risk.table.title="Number at risk", #the title to be used for the risk table.
title=NULL, # add title to plot
risk.table.height=.25,
add.all = FALSE,
combine = FALSE,
xlab="Time, months",
ylab="OS Probability",
axes.offset=TRUE,
surv.plot.height=1,
font.x = 15,
font.y = 15,
font.legend.title=15)
# Annotate the median OS and confidence interval for group
PVT_EHS_OS_plot$plot <- PVT_EHS_OS_plot$plot +
geom_label(aes(x = 37, y = 0.60,
label = paste("Absent \nMedian OS: ", round(PVT_EHS_median_OS_NO, 2), " months\n",
"95% CI: ", round(PVT_EHS_lower_OS_ci_NO, 2), "-", round(PVT_EHS_upper_OS_ci_NO, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D") +
geom_label(aes(x = 37, y = 0.90,
label = paste("Present \nMedian OS: ", round(PVT_EHS_median_OS_YES, 2), " months\n",
"95% CI: ", round(PVT_EHS_lower_OS_ci_YES, 2), "-", round(PVT_EHS_upper_OS_ci_YES, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4")
# Print the plot in the R Markdown document
print(PVT_EHS_OS_plot)
# High-resolution plot saving
png("PVT_OS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(PVT_EHS_OS_plot)
dev.off()## quartz_off_screen
## 2#### PVT_EHS Analysis
###For PVT_EHS survival curve the right hand side should be ~ PVT_EHS
PVT_EHS_PFS = survfit(Surv(Time_PFS, PFS_Status) ~ PVT , data = ClinicData)
summary_data_PVT_EHS_PFS <- summary(PVT_EHS_PFS)
# Extract median PFS and confidence intervals for each group
PVT_EHS_median_PFS_NO <- summary_data_PVT_EHS_PFS$table["PVT=No", "median"]
PVT_EHS_lower_PFS_ci_NO <- summary_data_PVT_EHS_PFS$table["PVT=No", "0.95LCL"]
PVT_EHS_upper_PFS_ci_NO <- summary_data_PVT_EHS_PFS$table["PVT=No", "0.95UCL"]
PVT_EHS_median_PFS_YES <- summary_data_PVT_EHS_PFS$table["PVT=Yes", "median"]
PVT_EHS_lower_PFS_ci_YES <- summary_data_PVT_EHS_PFS$table["PVT=Yes", "0.95LCL"]
PVT_EHS_upper_PFS_ci_YES <- summary_data_PVT_EHS_PFS$table["PVT=Yes", "0.95UCL"]
PVT_EHS_PFS_plot <- ggsurvplot(PVT_EHS_PFS,
conf.int=FALSE, # add confidence intervals
pval=TRUE, # show the p-value for the log-rank test
pval.method = TRUE, #corresponds to survival curves' comparison
pval.size= 4,# numeric value specifying the p-value text size. Default is 5.
pval.method.coord = c(30, 0.30), # <- move the "log-rank" method label here
pval.coord = c(30, 0.25),
test.for.trend = FALSE,
risk.table=TRUE,# show a risk table below the plot.
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", #for drawing a horizontal/vertical line at median survival.
legend.labs=c("Absent", "Present"), # change group labels
tables.col = "strata",
tables.y.text.col=TRUE,
fontsize=5,
legend.title="PVT", # add legend title
risk.table.title="Number at risk", #the title to be used for the risk table.
title=NULL, # add title to plot
risk.table.height=.25,
add.all = FALSE,
combine = FALSE,
xlab="Time, months",
ylab="PFS Probability",
axes.offset=TRUE,
surv.plot.height=1,
font.x = 15,
font.y = 15,
font.legend.title=15)
# Annotate the median PFS and confidence interval for group
PVT_EHS_PFS_plot$plot <- PVT_EHS_PFS_plot$plot +
geom_label(aes(x = 25, y = 0.60,
label = paste("Absent \nMedian PFS: ", round(PVT_EHS_median_PFS_NO, 2), " months\n",
"95% CI: ", round(PVT_EHS_lower_PFS_ci_NO, 2), "-", round(PVT_EHS_upper_PFS_ci_NO, 2))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D") +
geom_label(aes(x = 25, y = 0.90,
label = paste("Present \nMedian PFS: ", round(PVT_EHS_median_PFS_YES, 2), " months\n",
"95% CI: ", round(PVT_EHS_lower_PFS_ci_YES, 2), "-", round(PVT_EHS_upper_PFS_ci_YES, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4")
# Print the plot in the R Markdown document
print(PVT_EHS_PFS_plot)
# High-resolution plot saving
png("PVT_PFS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(PVT_EHS_PFS_plot)
dev.off()## quartz_off_screen
## 2# Create binary variable for cfDNA levels
cfDNA_Level_Baseline_binary <- ifelse(ClinicData$cfDNA_Level_Baseline_ng_ml <= 1200, "low", "high")
ClinicData$cfDNA_Level_Baseline_binary <- cfDNA_Level_Baseline_binary
# Fit the survival model for OS
cfDNA_Level_OS_fit <- survfit(Surv(Time_OS, OS_Status) ~ cfDNA_Level_Baseline_binary, data = ClinicData, conf.type = "log-log")
summary_data_cfDNA_Level_OS <- summary(cfDNA_Level_OS_fit)
# Extract median OS and confidence intervals for each group
cfDNA_Level_median_OS_low <- summary_data_cfDNA_Level_OS$time[summary_data_cfDNA_Level_OS$strata == "cfDNA_Level_Baseline_binary=low" & summary_data_cfDNA_Level_OS$time == summary_data_cfDNA_Level_OS$table["cfDNA_Level_Baseline_binary=low", "median"]]
cfDNA_Level_lower_OS_ci_low <- summary_data_cfDNA_Level_OS$table["cfDNA_Level_Baseline_binary=low", "0.95LCL"]
cfDNA_Level_upper_OS_ci_low <- summary_data_cfDNA_Level_OS$table["cfDNA_Level_Baseline_binary=low", "0.95UCL"]
cfDNA_Level_median_OS_high <- summary_data_cfDNA_Level_OS$time[summary_data_cfDNA_Level_OS$strata == "cfDNA_Level_Baseline_binary=high" & summary_data_cfDNA_Level_OS$time == summary_data_cfDNA_Level_OS$table["cfDNA_Level_Baseline_binary=high", "median"]]
cfDNA_Level_lower_OS_ci_high <- summary_data_cfDNA_Level_OS$table["cfDNA_Level_Baseline_binary=high", "0.95LCL"]
cfDNA_Level_upper_OS_ci_high <- summary_data_cfDNA_Level_OS$table["cfDNA_Level_Baseline_binary=high", "0.95UCL"]
# Create the survival plot
cfDNA_Level_OS_plot <- ggsurvplot(cfDNA_Level_OS_fit,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(45, 0.25), # <- move the "log-rank" method label here
pval.coord = c(45, 0.18),
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("≥ 1200", "< 1200"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "cfDNA level (ng/ml)", # add legend title
risk.table.title = "Number at risk", # risk table title
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "OS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median OS and confidence interval for low group
cfDNA_Level_OS_plot$plot <- cfDNA_Level_OS_plot$plot +
geom_label(aes(x = 35, y = 0.45,
label = paste("Low cfDNA level\nMedian OS: ", round(cfDNA_Level_median_OS_low, 1), " months\n",
"95% CI: ", round(cfDNA_Level_lower_OS_ci_low, 1), "-", round(cfDNA_Level_upper_OS_ci_low, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 35, y = 0.75,
label = paste("High cfDNA level\nMedian OS: ", round(cfDNA_Level_median_OS_high, 1), " months\n",
"95% CI: ", round(cfDNA_Level_lower_OS_ci_high, 1), "-", round(cfDNA_Level_upper_OS_ci_high, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")
# Print the plot in the R Markdown document
print(cfDNA_Level_OS_plot)
# High-resolution plot saving
png("cfDNA_Level_OS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(cfDNA_Level_OS_plot)
dev.off()## quartz_off_screen
## 2library(survival)
library(survminer)
# 1) Binary with desired order/labels
ClinicData$cfDNA_bin <- factor(
ifelse(ClinicData$cfDNA_Level_Baseline_ng_ml >= 1200, "≥ 1200", "< 1200"),
levels = c("≥ 1200","< 1200")
)
# 2) Fit
fit <- survfit(Surv(Time_PFS, PFS_Status) ~ cfDNA_bin, data = ClinicData, conf.type = "log-log")
# 3) Medians & CIs (robust)
med <- surv_median(fit) # from survminer
# rows are in the same order as factor levels above
med_hi <- med$median[med$strata=="cfDNA_bin=≥ 1200"]
ciL_hi <- med$lower[med$strata=="cfDNA_bin=≥ 1200"]
ciU_hi <- med$upper[med$strata=="cfDNA_bin=≥ 1200"]
med_lo <- med$median[med$strata=="cfDNA_bin=< 1200"]
ciL_lo <- med$lower[med$strata=="cfDNA_bin=< 1200"]
ciU_lo <- med$upper[med$strata=="cfDNA_bin=< 1200"]
# helper for pretty NA (median not reached)
fmt <- function(x) ifelse(is.na(x), "NA", sprintf("%.1f", x))
# 4) Plot (place log-rank text as you like)
p <- ggsurvplot(
fit,
pval = TRUE, pval.method = TRUE,
pval.method.coord = c(30, 0.25),
pval.coord = c(30, 0.18),
surv.median.line = "hv",
risk.table = TRUE,
legend.title = "cfDNA level (ng/ml)",
legend.labs = levels(ClinicData$cfDNA_bin),
xlab = "Time, months", ylab = "PFS Probability",
tables.col = "strata", tables.y.text.col = TRUE
)
# 5) Annotations (will show even if median is NA)
p$plot <- p$plot +
geom_label(aes(x=25, y=0.75,
label=paste0("High cfDNA level\nMedian PFS: ", fmt(med_hi), " months\n",
"95% CI: ", fmt(ciL_hi), " - ", fmt(ciU_hi))),
size=4, hjust=0, vjust=1, color="black", fill="#F8766D") +
geom_label(aes(x=25, y=0.45,
label=paste0("Low cfDNA level\nMedian PFS: ", fmt(med_lo), " months\n",
"95% CI: ", fmt(ciL_lo), " - ", fmt(ciU_lo))),
size=4, hjust=0, vjust=1, color="black", fill="#00BFC4")
print(p)
png("cfDNA_Level_OS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(p)
dev.off()## quartz_off_screen
## 2# Create binary variable for cfDNA Tumour Fraction (TF)
cfDNA_TF_Baseline_binary <- ifelse(ClinicData$Tumour_Fraction_ichorCNA_Baseline <= 0.10, "low", "high")
ClinicData$cfDNA_TF_Baseline_binary <- cfDNA_TF_Baseline_binary
# Fit the survival model for OS
cfDNA_TF_OS_fit <- survfit(Surv(Time_OS, OS_Status) ~ cfDNA_TF_Baseline_binary, data = ClinicData, conf.type = "log-log")
cfDNA_TF_summary_data_OS <- summary(cfDNA_TF_OS_fit)
# Extract median OS and confidence intervals for each group
cfDNA_TF_median_OS_low <- cfDNA_TF_summary_data_OS$table["cfDNA_TF_Baseline_binary=low", "median"]
cfDNA_TF_lower_OS_ci_low <- cfDNA_TF_summary_data_OS$table["cfDNA_TF_Baseline_binary=low", "0.95LCL"]
cfDNA_TF_upper_OS_ci_low <- cfDNA_TF_summary_data_OS$table["cfDNA_TF_Baseline_binary=low", "0.95UCL"]
cfDNA_TF_median_OS_high <- cfDNA_TF_summary_data_OS$table["cfDNA_TF_Baseline_binary=high", "median"]
cfDNA_TF_lower_OS_ci_high <- cfDNA_TF_summary_data_OS$table["cfDNA_TF_Baseline_binary=high", "0.95LCL"]
cfDNA_TF_upper_OS_ci_high <- cfDNA_TF_summary_data_OS$table["cfDNA_TF_Baseline_binary=high", "0.95UCL"]
# Create the survival plot
cfDNA_TF_OS_plot <- ggsurvplot(cfDNA_TF_OS_fit,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(40, 0.30), # <- move the "log-rank" method label here
pval.coord = c(40, 0.25), # <- move the log-rank p-value here (x, y)
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("≥ 10%", "< 10%"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "Tumour Fraction", # add legend title
risk.table.title = "Number at risk", # risk table title
risk.table.fontsize = 5,
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "OS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median OS and confidence interval for low group
cfDNA_TF_OS_plot$plot <- cfDNA_TF_OS_plot$plot +
geom_label(aes(x = 35, y = 0.50,
label = paste("Low TF\nMedian OS: ", round(cfDNA_TF_median_OS_low, 1), " months\n",
"95% CI: ", round(cfDNA_TF_lower_OS_ci_low, 1), "-", round(cfDNA_TF_upper_OS_ci_low, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 35, y = 0.80,
label = paste("High TF\nMedian OS: ", cfDNA_TF_median_OS_high, " months\n",
"95% CI: ", round(cfDNA_TF_lower_OS_ci_high, 1), "-", round(cfDNA_TF_upper_OS_ci_high, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")
# Print the plot in the R Markdown document
print(cfDNA_TF_OS_plot)
# High-resolution plot saving
png("cfDNA_TF_OS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(cfDNA_TF_OS_plot)
dev.off()## quartz_off_screen
## 2# Create binary variable for cfDNA Tumour Fraction (TF)
cfDNA_TF_Baseline_binary <- ifelse(ClinicData$Tumour_Fraction_ichorCNA_Baseline <= 0.10, "low", "high")
ClinicData$cfDNA_TF_Baseline_binary <- cfDNA_TF_Baseline_binary
# Fit the survival model for PFS
cfDNA_TF_PFS_fit <- survfit(Surv(Time_PFS, PFS_Status) ~ cfDNA_TF_Baseline_binary, data = ClinicData, conf.type = "log-log")
cfDNA_TF_summary_data_PFS <- summary(cfDNA_TF_PFS_fit)
# Extract median PFS and confidence intervals for each group
cfDNA_TF_median_PFS_low <- cfDNA_TF_summary_data_PFS$table["cfDNA_TF_Baseline_binary=low", "median"]
cfDNA_TF_lower_PFS_ci_low <- cfDNA_TF_summary_data_PFS$table["cfDNA_TF_Baseline_binary=low", "0.95LCL"]
cfDNA_TF_upper_PFS_ci_low <- cfDNA_TF_summary_data_PFS$table["cfDNA_TF_Baseline_binary=low", "0.95UCL"]
cfDNA_TF_median_PFS_high <- cfDNA_TF_summary_data_PFS$table["cfDNA_TF_Baseline_binary=high", "median"]
cfDNA_TF_lower_PFS_ci_high <- cfDNA_TF_summary_data_PFS$table["cfDNA_TF_Baseline_binary=high", "0.95LCL"]
cfDNA_TF_upper_PFS_ci_high <- cfDNA_TF_summary_data_PFS$table["cfDNA_TF_Baseline_binary=high", "0.95UCL"]
# Create the survival plot
cfDNA_TF_PFS_plot <- ggsurvplot(cfDNA_TF_PFS_fit,
conf.int = FALSE, # add confidence intervals
pval = TRUE, # show the p-value for the log-rank test
pval.method = TRUE, # show method for p-value
pval.size = 4, # p-value text size
pval.method.coord = c(30, 0.30), # <- move the "log-rank" method label here
pval.coord = c(30, 0.25), # <- move the log-rank p-value here (x, y)
test.for.trend = FALSE,
risk.table = TRUE, # show a risk table below the plot
cumevents = FALSE,
cumcensor = FALSE,
surv.median.line = "hv", # horizontal/vertical line at median survival
legend.labs = c("≥ 10%", "< 10%"), # change group labels
tables.col = "strata",
tables.y.text.col = TRUE,
fontsize = 5,
legend.title = "Tumour Fraction", # add legend title
risk.table.title = "Number at risk", # risk table title
risk.table.fontsize = 5,
title = NULL, # plot title
risk.table.height = 0.30,
add.all = FALSE,
combine = FALSE,
xlab = "Time, months",
ylab = "PFS Probability",
axes.offset = TRUE,
surv.plot.height = 1,
font.x = 15,
font.y = 15,
font.legend.title = 15)
# Annotate the median PFS and confidence interval for low group
cfDNA_TF_PFS_plot$plot <- cfDNA_TF_PFS_plot$plot +
geom_label(aes(x = 25, y = 0.50,
label = paste("Low TF\nMedian PFS: ", round(cfDNA_TF_median_PFS_low, 1), " months\n",
"95% CI: ", round(cfDNA_TF_lower_PFS_ci_low, 1), "-", round(cfDNA_TF_upper_PFS_ci_low, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#00BFC4") +
geom_label(aes(x = 25, y = 0.80,
label = paste("High TF\nMedian PFS: ", cfDNA_TF_median_PFS_high, " months\n",
"95% CI: ", round(cfDNA_TF_lower_PFS_ci_high, 1), "-", round(cfDNA_TF_upper_PFS_ci_high, 1))),
size = 4, hjust = 0, vjust = 1, color = "black", fill = "#F8766D")
# Print the plot in the R Markdown document
print(cfDNA_TF_PFS_plot)
# High-resolution plot saving
png("cfDNA_TF_PFS_TKI_ICI.png", width = 8, height = 8, units = "in", res = 500)
print(cfDNA_TF_PFS_plot)
dev.off()## quartz_off_screen
## 2library(dplyr)
library(survival)
library(gtsummary)
# Ensure AFP is numeric, then create binary: >400 = high, ≤400 = low
ClinicData <- ClinicData %>%
mutate(
AFP_num = suppressWarnings(as.numeric(AFP)),
AFP_binary = factor(ifelse(AFP_num > 400, "high", "low"), levels = c("low","high"))
)
# Cox models
cox_model_OS_AFP <- coxph(Surv(Time_OS, OS_Status) ~ AFP_binary, data = ClinicData) %>%
tbl_regression(exp = TRUE) %>%
modify_header(label = "Effect of AFP >400 μg/L ('high') vs ≤400 μg/L ('low') on OS")
cox_model_PFS_AFP <- coxph(Surv(Time_PFS, PFS_Status) ~ AFP_binary, data = ClinicData) %>%
tbl_regression(exp = TRUE) %>%
modify_header(label = "Effect of AFP >400 μg/L ('high') vs ≤400 μg/L ('low') on PFS")
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## font-size: 100%;
## vertical-align: 0;
## }
##
## #vexispxelv .gt_indent_1 {
## text-indent: 5px;
## }
##
## #vexispxelv .gt_indent_2 {
## text-indent: 10px;
## }
##
## #vexispxelv .gt_indent_3 {
## text-indent: 15px;
## }
##
## #vexispxelv .gt_indent_4 {
## text-indent: 20px;
## }
##
## #vexispxelv .gt_indent_5 {
## text-indent: 25px;
## }
##
## #vexispxelv .katex-display {
## display: inline-flex !important;
## margin-bottom: 0.75em !important;
## }
##
## #vexispxelv div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
## height: 0px !important;
## }
## </style>
## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Effect of AFP >400 μg/L (‘high’) vs ≤400 μg/L (‘low’) on OS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">AFP_binary</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> low</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> high</td>
## <td headers="estimate" class="gt_row gt_center">2.90</td>
## <td headers="conf.low" class="gt_row gt_center">1.87, 4.48</td>
## <td headers="p.value" class="gt_row gt_center"><0.001</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
## </div>## <div id="cwgehvsltg" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
## <style>#cwgehvsltg table {
## font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
## -webkit-font-smoothing: antialiased;
## -moz-osx-font-smoothing: grayscale;
## }
##
## #cwgehvsltg thead, #cwgehvsltg tbody, #cwgehvsltg tfoot, #cwgehvsltg tr, #cwgehvsltg td, #cwgehvsltg th {
## border-style: none;
## }
##
## #cwgehvsltg p {
## margin: 0;
## padding: 0;
## }
##
## #cwgehvsltg .gt_table {
## display: table;
## border-collapse: collapse;
## line-height: normal;
## margin-left: auto;
## margin-right: auto;
## color: #333333;
## font-size: 16px;
## font-weight: normal;
## font-style: normal;
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## border-top-width: 2px;
## border-top-color: #A8A8A8;
## border-right-style: none;
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## border-bottom-width: 2px;
## border-bottom-color: #A8A8A8;
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## }
##
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## padding-top: 4px;
## padding-bottom: 4px;
## }
##
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## color: #333333;
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## }
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## color: #333333;
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## padding-bottom: 5px;
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##
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## background-color: #FFFFFF;
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## border-bottom-style: solid;
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## }
##
## #cwgehvsltg .gt_col_heading {
## color: #333333;
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## color: #333333;
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##
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## padding-right: 0;
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##
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## padding-bottom: 5px;
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## display: inline-block;
## width: 100%;
## }
##
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## border-bottom-style: hidden;
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##
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## padding-top: 8px;
## padding-bottom: 8px;
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## padding-right: 5px;
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## background-color: #FFFFFF;
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## text-transform: inherit;
## border-top-style: solid;
## border-top-width: 2px;
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## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
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## text-align: left;
## }
##
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## padding: 0.5px;
## color: #333333;
## background-color: #FFFFFF;
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## border-top-style: solid;
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## border-top-color: #D3D3D3;
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## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## vertical-align: middle;
## }
##
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## margin-top: 0;
## }
##
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## margin-bottom: 0;
## }
##
## #cwgehvsltg .gt_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## margin: 10px;
## border-top-style: solid;
## border-top-width: 1px;
## border-top-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## vertical-align: middle;
## overflow-x: hidden;
## }
##
## #cwgehvsltg .gt_stub {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
## border-right-style: solid;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #cwgehvsltg .gt_stub_row_group {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
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## border-right-width: 2px;
## border-right-color: #D3D3D3;
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## padding-right: 5px;
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## }
##
## #cwgehvsltg .gt_row_group_first td {
## border-top-width: 2px;
## }
##
## #cwgehvsltg .gt_row_group_first th {
## border-top-width: 2px;
## }
##
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## color: #333333;
## background-color: #FFFFFF;
## text-transform: inherit;
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #cwgehvsltg .gt_first_summary_row {
## border-top-style: solid;
## border-top-color: #D3D3D3;
## }
##
## #cwgehvsltg .gt_first_summary_row.thick {
## border-top-width: 2px;
## }
##
## #cwgehvsltg .gt_last_summary_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #cwgehvsltg .gt_grand_summary_row {
## color: #333333;
## background-color: #FFFFFF;
## text-transform: inherit;
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #cwgehvsltg .gt_first_grand_summary_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-top-style: double;
## border-top-width: 6px;
## border-top-color: #D3D3D3;
## }
##
## #cwgehvsltg .gt_last_grand_summary_row_top {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-style: double;
## border-bottom-width: 6px;
## border-bottom-color: #D3D3D3;
## }
##
## #cwgehvsltg .gt_striped {
## background-color: rgba(128, 128, 128, 0.05);
## }
##
## #cwgehvsltg .gt_table_body {
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #cwgehvsltg .gt_footnotes {
## color: #333333;
## background-color: #FFFFFF;
## border-bottom-style: none;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## }
##
## #cwgehvsltg .gt_footnote {
## margin: 0px;
## font-size: 90%;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #cwgehvsltg .gt_sourcenotes {
## color: #333333;
## background-color: #FFFFFF;
## border-bottom-style: none;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## }
##
## #cwgehvsltg .gt_sourcenote {
## font-size: 90%;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #cwgehvsltg .gt_left {
## text-align: left;
## }
##
## #cwgehvsltg .gt_center {
## text-align: center;
## }
##
## #cwgehvsltg .gt_right {
## text-align: right;
## font-variant-numeric: tabular-nums;
## }
##
## #cwgehvsltg .gt_font_normal {
## font-weight: normal;
## }
##
## #cwgehvsltg .gt_font_bold {
## font-weight: bold;
## }
##
## #cwgehvsltg .gt_font_italic {
## font-style: italic;
## }
##
## #cwgehvsltg .gt_super {
## font-size: 65%;
## }
##
## #cwgehvsltg .gt_footnote_marks {
## font-size: 75%;
## vertical-align: 0.4em;
## position: initial;
## }
##
## #cwgehvsltg .gt_asterisk {
## font-size: 100%;
## vertical-align: 0;
## }
##
## #cwgehvsltg .gt_indent_1 {
## text-indent: 5px;
## }
##
## #cwgehvsltg .gt_indent_2 {
## text-indent: 10px;
## }
##
## #cwgehvsltg .gt_indent_3 {
## text-indent: 15px;
## }
##
## #cwgehvsltg .gt_indent_4 {
## text-indent: 20px;
## }
##
## #cwgehvsltg .gt_indent_5 {
## text-indent: 25px;
## }
##
## #cwgehvsltg .katex-display {
## display: inline-flex !important;
## margin-bottom: 0.75em !important;
## }
##
## #cwgehvsltg div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
## height: 0px !important;
## }
## </style>
## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Effect of AFP >400 μg/L (‘high’) vs ≤400 μg/L (‘low’) on PFS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">AFP_binary</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> low</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> high</td>
## <td headers="estimate" class="gt_row gt_center">1.98</td>
## <td headers="conf.low" class="gt_row gt_center">1.31, 3.00</td>
## <td headers="p.value" class="gt_row gt_center">0.001</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
## </div>ClinicData$Best_Responsce <- factor(ClinicData$Best_Responsce)
ClinicData$Best_Responsce <- relevel(ClinicData$Best_Responsce, ref = "SD")
# Fit the Cox proportional hazards model for Overall Survival
(cox_model_OS_Responsce <- coxph(Surv(Time_OS, OS_Status) ~ Best_Responsce, data = ClinicData) %>%
tbl_regression(exp = TRUE)%>%
modify_header(label = "Effect of best responsce on OS "))| Effect of best responsce on OS | HR | 95% CI | p-value |
|---|---|---|---|
| Best_Responsce | |||
| SD | — | — | |
| PD | 2.94 | 1.97, 4.41 | <0.001 |
| Abbreviations: CI = Confidence Interval, HR = Hazard Ratio | |||
# Fit the Cox proportional hazards model for Progression-Free Survival
(cox_model_PFS_Responsce <- coxph(Surv(Time_PFS, PFS_Status) ~ Best_Responsce, data = ClinicData) %>%
tbl_regression(exp = TRUE)%>%
modify_header(label = "Effect of best responsce on PFS"))| Effect of best responsce on PFS | HR | 95% CI | p-value |
|---|---|---|---|
| Best_Responsce | |||
| SD | — | — | |
| PD | 216 | 49.0, 956 | <0.001 |
| Abbreviations: CI = Confidence Interval, HR = Hazard Ratio | |||
library(dplyr)
library(survival)
library(gtsummary)
# Ensure unordered factor and set reference = "B"
ClinicData <- ClinicData %>%
mutate(
BCLC = as.character(BCLC),
BCLC = factor(BCLC)
)
if ("B" %in% levels(ClinicData$BCLC)) {
ClinicData$BCLC <- relevel(ClinicData$BCLC, ref = "B")
} else {
message("⚠️ 'B' not found in BCLC levels: ", paste(levels(ClinicData$BCLC), collapse = ", "))
}
# Cox PH models
cox_model_OS_BCLC <- coxph(Surv(Time_OS, OS_Status) ~ BCLC, data = ClinicData) %>%
tbl_regression(exp = TRUE) %>%
modify_header(label = "Effect of BCLC stage on OS")
cox_model_PFS_BCLC <- coxph(Surv(Time_PFS, PFS_Status) ~ BCLC, data = ClinicData) %>%
tbl_regression(exp = TRUE) %>%
modify_header(label = "Effect of BCLC stage on PFS")
print(cox_model_OS_BCLC)## <div id="xutnystdhu" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
## <style>#xutnystdhu table {
## font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
## -webkit-font-smoothing: antialiased;
## -moz-osx-font-smoothing: grayscale;
## }
##
## #xutnystdhu thead, #xutnystdhu tbody, #xutnystdhu tfoot, #xutnystdhu tr, #xutnystdhu td, #xutnystdhu th {
## border-style: none;
## }
##
## #xutnystdhu p {
## margin: 0;
## padding: 0;
## }
##
## #xutnystdhu .gt_table {
## display: table;
## border-collapse: collapse;
## line-height: normal;
## margin-left: auto;
## margin-right: auto;
## color: #333333;
## font-size: 16px;
## font-weight: normal;
## font-style: normal;
## background-color: #FFFFFF;
## width: auto;
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #A8A8A8;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #A8A8A8;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## }
##
## #xutnystdhu .gt_caption {
## padding-top: 4px;
## padding-bottom: 4px;
## }
##
## #xutnystdhu .gt_title {
## color: #333333;
## font-size: 125%;
## font-weight: initial;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-color: #FFFFFF;
## border-bottom-width: 0;
## }
##
## #xutnystdhu .gt_subtitle {
## color: #333333;
## font-size: 85%;
## font-weight: initial;
## padding-top: 3px;
## padding-bottom: 5px;
## padding-left: 5px;
## padding-right: 5px;
## border-top-color: #FFFFFF;
## border-top-width: 0;
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## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Effect of BCLC stage on OS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
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## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
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## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> B</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> C</td>
## <td headers="estimate" class="gt_row gt_center">2.39</td>
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## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
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## text-indent: 25px;
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## display: inline-flex !important;
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## height: 0px !important;
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## </style>
## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Effect of BCLC stage on PFS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">BCLC</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> B</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> C</td>
## <td headers="estimate" class="gt_row gt_center">2.96</td>
## <td headers="conf.low" class="gt_row gt_center">1.93, 4.56</td>
## <td headers="p.value" class="gt_row gt_center"><0.001</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
## </div># Convert Age_binary to factor and set reference level to "low"
ClinicData$Age_binary <- factor(ClinicData$Age_binary)
ClinicData$Age_binary <- relevel(ClinicData$Age_binary, ref = "low")
# Fit the Cox proportional hazards model for Overall Survival
(cox_model_OS_Age <- coxph(Surv(Time_OS, OS_Status) ~ Age_binary, data = ClinicData) %>%
tbl_regression(exp = TRUE)%>%
modify_header(label = "Effect of Age (years) > 70 'high' versus ≤ 70 'low' on OS"))| Effect of Age (years) > 70 ‘high’ versus ≤ 70 ‘low’ on OS | HR | 95% CI | p-value |
|---|---|---|---|
| Age_binary | |||
| low | — | — | |
| high | 0.96 | 0.65, 1.42 | 0.8 |
| Abbreviations: CI = Confidence Interval, HR = Hazard Ratio | |||
# Fit the Cox proportional hazards model for Progression-Free Survival
(cox_model_PFS_Age <- coxph(Surv(Time_PFS, PFS_Status) ~ Age_binary, data = ClinicData) %>%
tbl_regression(exp = TRUE)%>%
modify_header(label = "Effect of Age (years) > 70 'high' versus ≤ 70 'low' on PFS"))| Effect of Age (years) > 70 ‘high’ versus ≤ 70 ‘low’ on PFS | HR | 95% CI | p-value |
|---|---|---|---|
| Age_binary | |||
| low | — | — | |
| high | 0.98 | 0.68, 1.42 | >0.9 |
| Abbreviations: CI = Confidence Interval, HR = Hazard Ratio | |||
# Convert Age_binary to factor and set reference level to "low"
ClinicData$TumourSize_binary <- factor(ClinicData$TumourSize_binary)## Error in `$<-.data.frame`(`*tmp*`, TumourSize_binary, value = structure(integer(0), levels = character(0), class = "factor")): replacement has 0 rows, data has 134## Error in relevel.default(ClinicData$TumourSize_binary, ref = "low"): 'relevel' only for (unordered) factors# Fit the Cox proportional hazards model for Overall Survival
(cox_model_OS_TumourSize <- coxph(Surv(Time_OS, OS_Status) ~ TumourSize_binary, data = ClinicData) %>%
tbl_regression(exp = TRUE)%>%
modify_header(label = "Effect of Tumour size (cm) > 7 'high' versus ≤ 7 'low' on OS "))| Effect of Tumour size (cm) > 7 ‘high’ versus ≤ 7 ‘low’ on OS | HR | 95% CI | p-value |
|---|---|---|---|
| TumourSize_binary | |||
| high | — | — | |
| low | 0.77 | 0.52, 1.15 | 0.2 |
| Abbreviations: CI = Confidence Interval, HR = Hazard Ratio | |||
# Fit the Cox proportional hazards model for Progression-Free Survival
(cox_model_PFS_TumourSize <- coxph(Surv(Time_PFS, PFS_Status) ~ TumourSize_binary, data = ClinicData) %>%
tbl_regression(exp = TRUE)%>%
modify_header(label = "Effect of Tumour size (cm) > 7 'high' versus ≤ 7 'low' on PFS "))| Effect of Tumour size (cm) > 7 ‘high’ versus ≤ 7 ‘low’ on PFS | HR | 95% CI | p-value |
|---|---|---|---|
| TumourSize_binary | |||
| high | — | — | |
| low | 0.72 | 0.50, 1.04 | 0.083 |
| Abbreviations: CI = Confidence Interval, HR = Hazard Ratio | |||
library(dplyr)
library(survival)
library(gtsummary)
# 1️⃣ Ensure CTP variable is a clean unordered factor with "A" as the reference
ClinicData <- ClinicData %>%
mutate(
CTP = as.character(CTP), # Remove ordered factor if present
CTP = factor(CTP), # Convert to unordered factor
CTP = relevel(CTP, ref = "A") # Set "A" as reference level
)
# ✅ Optional: check levels
print(levels(ClinicData$CTP))## [1] "A" "B"# 2️⃣ Cox model for Overall Survival (OS)
cox_model_OS_CTP <- coxph(Surv(Time_OS, OS_Status) ~ CTP, data = ClinicData) %>%
tbl_regression(exp = TRUE) %>%
modify_header(label = "Effect of CTP Class on OS")
# 3️⃣ Cox model for Progression-Free Survival (PFS)
cox_model_PFS_CTP <- coxph(Surv(Time_PFS, PFS_Status) ~ CTP, data = ClinicData) %>%
tbl_regression(exp = TRUE) %>%
modify_header(label = "Effect of CTP Class on PFS")
# 4️⃣ Print both results
print(cox_model_OS_CTP)## <div id="ugvjsqgdwc" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
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## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Effect of CTP Class on OS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">CTP</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> A</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> B</td>
## <td headers="estimate" class="gt_row gt_center">2.05</td>
## <td headers="conf.low" class="gt_row gt_center">1.18, 3.58</td>
## <td headers="p.value" class="gt_row gt_center">0.011</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
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## height: 0px !important;
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## </style>
## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Effect of CTP Class on PFS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">CTP</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> A</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> B</td>
## <td headers="estimate" class="gt_row gt_center">2.40</td>
## <td headers="conf.low" class="gt_row gt_center">1.39, 4.14</td>
## <td headers="p.value" class="gt_row gt_center">0.002</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
## </div>library(dplyr)
library(survival)
library(gtsummary)
# 1️⃣ Ensure PS is an unordered factor before releveling
ClinicData <- ClinicData %>%
mutate(
PS = as.character(PS), # Convert to character (removes ordering)
PS = factor(PS), # Convert to unordered factor
PS = relevel(PS, ref = "0") # Set "0" as reference level
)
# 🛑 Optional: Check available levels
print(levels(ClinicData$PS))## [1] "0" "1" "2"# 2️⃣ Cox model for Overall Survival (OS)
cox_model_OS_PS <- coxph(Surv(Time_OS, OS_Status) ~ PS, data = ClinicData) %>%
tbl_regression(exp = TRUE) %>%
modify_header(label = "Effect of Performance Status (PS) on OS")
# 3️⃣ Cox model for Progression-Free Survival (PFS)
cox_model_PFS_PS <- coxph(Surv(Time_PFS, PFS_Status) ~ PS, data = ClinicData) %>%
tbl_regression(exp = TRUE) %>%
modify_header(label = "Effect of Performance Status (PS) on PFS")
# 4️⃣ Print results
print(cox_model_OS_PS)## <div id="rjzmmscshd" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
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## -webkit-font-smoothing: antialiased;
## -moz-osx-font-smoothing: grayscale;
## }
##
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## }
##
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## margin: 0;
## padding: 0;
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##
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## display: table;
## border-collapse: collapse;
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## margin-right: auto;
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## font-weight: normal;
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## border-top-width: 2px;
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## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## vertical-align: bottom;
## padding-top: 5px;
## padding-bottom: 5px;
## overflow-x: hidden;
## display: inline-block;
## width: 100%;
## }
##
## #rjzmmscshd .gt_spanner_row {
## border-bottom-style: hidden;
## }
##
## #rjzmmscshd .gt_group_heading {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## vertical-align: middle;
## text-align: left;
## }
##
## #rjzmmscshd .gt_empty_group_heading {
## padding: 0.5px;
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## vertical-align: middle;
## }
##
## #rjzmmscshd .gt_from_md > :first-child {
## margin-top: 0;
## }
##
## #rjzmmscshd .gt_from_md > :last-child {
## margin-bottom: 0;
## }
##
## #rjzmmscshd .gt_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## margin: 10px;
## border-top-style: solid;
## border-top-width: 1px;
## border-top-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## vertical-align: middle;
## overflow-x: hidden;
## }
##
## #rjzmmscshd .gt_stub {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
## border-right-style: solid;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #rjzmmscshd .gt_stub_row_group {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
## border-right-style: solid;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## padding-left: 5px;
## padding-right: 5px;
## vertical-align: top;
## }
##
## #rjzmmscshd .gt_row_group_first td {
## border-top-width: 2px;
## }
##
## #rjzmmscshd .gt_row_group_first th {
## border-top-width: 2px;
## }
##
## #rjzmmscshd .gt_summary_row {
## color: #333333;
## background-color: #FFFFFF;
## text-transform: inherit;
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #rjzmmscshd .gt_first_summary_row {
## border-top-style: solid;
## border-top-color: #D3D3D3;
## }
##
## #rjzmmscshd .gt_first_summary_row.thick {
## border-top-width: 2px;
## }
##
## #rjzmmscshd .gt_last_summary_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #rjzmmscshd .gt_grand_summary_row {
## color: #333333;
## background-color: #FFFFFF;
## text-transform: inherit;
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #rjzmmscshd .gt_first_grand_summary_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-top-style: double;
## border-top-width: 6px;
## border-top-color: #D3D3D3;
## }
##
## #rjzmmscshd .gt_last_grand_summary_row_top {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-style: double;
## border-bottom-width: 6px;
## border-bottom-color: #D3D3D3;
## }
##
## #rjzmmscshd .gt_striped {
## background-color: rgba(128, 128, 128, 0.05);
## }
##
## #rjzmmscshd .gt_table_body {
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #rjzmmscshd .gt_footnotes {
## color: #333333;
## background-color: #FFFFFF;
## border-bottom-style: none;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## }
##
## #rjzmmscshd .gt_footnote {
## margin: 0px;
## font-size: 90%;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #rjzmmscshd .gt_sourcenotes {
## color: #333333;
## background-color: #FFFFFF;
## border-bottom-style: none;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## }
##
## #rjzmmscshd .gt_sourcenote {
## font-size: 90%;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #rjzmmscshd .gt_left {
## text-align: left;
## }
##
## #rjzmmscshd .gt_center {
## text-align: center;
## }
##
## #rjzmmscshd .gt_right {
## text-align: right;
## font-variant-numeric: tabular-nums;
## }
##
## #rjzmmscshd .gt_font_normal {
## font-weight: normal;
## }
##
## #rjzmmscshd .gt_font_bold {
## font-weight: bold;
## }
##
## #rjzmmscshd .gt_font_italic {
## font-style: italic;
## }
##
## #rjzmmscshd .gt_super {
## font-size: 65%;
## }
##
## #rjzmmscshd .gt_footnote_marks {
## font-size: 75%;
## vertical-align: 0.4em;
## position: initial;
## }
##
## #rjzmmscshd .gt_asterisk {
## font-size: 100%;
## vertical-align: 0;
## }
##
## #rjzmmscshd .gt_indent_1 {
## text-indent: 5px;
## }
##
## #rjzmmscshd .gt_indent_2 {
## text-indent: 10px;
## }
##
## #rjzmmscshd .gt_indent_3 {
## text-indent: 15px;
## }
##
## #rjzmmscshd .gt_indent_4 {
## text-indent: 20px;
## }
##
## #rjzmmscshd .gt_indent_5 {
## text-indent: 25px;
## }
##
## #rjzmmscshd .katex-display {
## display: inline-flex !important;
## margin-bottom: 0.75em !important;
## }
##
## #rjzmmscshd div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
## height: 0px !important;
## }
## </style>
## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Effect of Performance Status (PS) on OS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">PS</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 0</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 1</td>
## <td headers="estimate" class="gt_row gt_center">1.56</td>
## <td headers="conf.low" class="gt_row gt_center">1.02, 2.39</td>
## <td headers="p.value" class="gt_row gt_center">0.042</td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 2</td>
## <td headers="estimate" class="gt_row gt_center">4.30</td>
## <td headers="conf.low" class="gt_row gt_center">2.01, 9.18</td>
## <td headers="p.value" class="gt_row gt_center"><0.001</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
## </div>## <div id="sbuahhbmwf" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
## <style>#sbuahhbmwf table {
## font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
## -webkit-font-smoothing: antialiased;
## -moz-osx-font-smoothing: grayscale;
## }
##
## #sbuahhbmwf thead, #sbuahhbmwf tbody, #sbuahhbmwf tfoot, #sbuahhbmwf tr, #sbuahhbmwf td, #sbuahhbmwf th {
## border-style: none;
## }
##
## #sbuahhbmwf p {
## margin: 0;
## padding: 0;
## }
##
## #sbuahhbmwf .gt_table {
## display: table;
## border-collapse: collapse;
## line-height: normal;
## margin-left: auto;
## margin-right: auto;
## color: #333333;
## font-size: 16px;
## font-weight: normal;
## font-style: normal;
## background-color: #FFFFFF;
## width: auto;
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #A8A8A8;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #A8A8A8;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## }
##
## #sbuahhbmwf .gt_caption {
## padding-top: 4px;
## padding-bottom: 4px;
## }
##
## #sbuahhbmwf .gt_title {
## color: #333333;
## font-size: 125%;
## font-weight: initial;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-color: #FFFFFF;
## border-bottom-width: 0;
## }
##
## #sbuahhbmwf .gt_subtitle {
## color: #333333;
## font-size: 85%;
## font-weight: initial;
## padding-top: 3px;
## padding-bottom: 5px;
## padding-left: 5px;
## padding-right: 5px;
## border-top-color: #FFFFFF;
## border-top-width: 0;
## }
##
## #sbuahhbmwf .gt_heading {
## background-color: #FFFFFF;
## text-align: center;
## border-bottom-color: #FFFFFF;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## }
##
## #sbuahhbmwf .gt_bottom_border {
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #sbuahhbmwf .gt_col_headings {
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## }
##
## #sbuahhbmwf .gt_col_heading {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: normal;
## text-transform: inherit;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## vertical-align: bottom;
## padding-top: 5px;
## padding-bottom: 6px;
## padding-left: 5px;
## padding-right: 5px;
## overflow-x: hidden;
## }
##
## #sbuahhbmwf .gt_column_spanner_outer {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: normal;
## text-transform: inherit;
## padding-top: 0;
## padding-bottom: 0;
## padding-left: 4px;
## padding-right: 4px;
## }
##
## #sbuahhbmwf .gt_column_spanner_outer:first-child {
## padding-left: 0;
## }
##
## #sbuahhbmwf .gt_column_spanner_outer:last-child {
## padding-right: 0;
## }
##
## #sbuahhbmwf .gt_column_spanner {
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## vertical-align: bottom;
## padding-top: 5px;
## padding-bottom: 5px;
## overflow-x: hidden;
## display: inline-block;
## width: 100%;
## }
##
## #sbuahhbmwf .gt_spanner_row {
## border-bottom-style: hidden;
## }
##
## #sbuahhbmwf .gt_group_heading {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## vertical-align: middle;
## text-align: left;
## }
##
## #sbuahhbmwf .gt_empty_group_heading {
## padding: 0.5px;
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## vertical-align: middle;
## }
##
## #sbuahhbmwf .gt_from_md > :first-child {
## margin-top: 0;
## }
##
## #sbuahhbmwf .gt_from_md > :last-child {
## margin-bottom: 0;
## }
##
## #sbuahhbmwf .gt_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## margin: 10px;
## border-top-style: solid;
## border-top-width: 1px;
## border-top-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## vertical-align: middle;
## overflow-x: hidden;
## }
##
## #sbuahhbmwf .gt_stub {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
## border-right-style: solid;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #sbuahhbmwf .gt_stub_row_group {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
## border-right-style: solid;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## padding-left: 5px;
## padding-right: 5px;
## vertical-align: top;
## }
##
## #sbuahhbmwf .gt_row_group_first td {
## border-top-width: 2px;
## }
##
## #sbuahhbmwf .gt_row_group_first th {
## border-top-width: 2px;
## }
##
## #sbuahhbmwf .gt_summary_row {
## color: #333333;
## background-color: #FFFFFF;
## text-transform: inherit;
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #sbuahhbmwf .gt_first_summary_row {
## border-top-style: solid;
## border-top-color: #D3D3D3;
## }
##
## #sbuahhbmwf .gt_first_summary_row.thick {
## border-top-width: 2px;
## }
##
## #sbuahhbmwf .gt_last_summary_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #sbuahhbmwf .gt_grand_summary_row {
## color: #333333;
## background-color: #FFFFFF;
## text-transform: inherit;
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #sbuahhbmwf .gt_first_grand_summary_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-top-style: double;
## border-top-width: 6px;
## border-top-color: #D3D3D3;
## }
##
## #sbuahhbmwf .gt_last_grand_summary_row_top {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-style: double;
## border-bottom-width: 6px;
## border-bottom-color: #D3D3D3;
## }
##
## #sbuahhbmwf .gt_striped {
## background-color: rgba(128, 128, 128, 0.05);
## }
##
## #sbuahhbmwf .gt_table_body {
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #sbuahhbmwf .gt_footnotes {
## color: #333333;
## background-color: #FFFFFF;
## border-bottom-style: none;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## }
##
## #sbuahhbmwf .gt_footnote {
## margin: 0px;
## font-size: 90%;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #sbuahhbmwf .gt_sourcenotes {
## color: #333333;
## background-color: #FFFFFF;
## border-bottom-style: none;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## }
##
## #sbuahhbmwf .gt_sourcenote {
## font-size: 90%;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #sbuahhbmwf .gt_left {
## text-align: left;
## }
##
## #sbuahhbmwf .gt_center {
## text-align: center;
## }
##
## #sbuahhbmwf .gt_right {
## text-align: right;
## font-variant-numeric: tabular-nums;
## }
##
## #sbuahhbmwf .gt_font_normal {
## font-weight: normal;
## }
##
## #sbuahhbmwf .gt_font_bold {
## font-weight: bold;
## }
##
## #sbuahhbmwf .gt_font_italic {
## font-style: italic;
## }
##
## #sbuahhbmwf .gt_super {
## font-size: 65%;
## }
##
## #sbuahhbmwf .gt_footnote_marks {
## font-size: 75%;
## vertical-align: 0.4em;
## position: initial;
## }
##
## #sbuahhbmwf .gt_asterisk {
## font-size: 100%;
## vertical-align: 0;
## }
##
## #sbuahhbmwf .gt_indent_1 {
## text-indent: 5px;
## }
##
## #sbuahhbmwf .gt_indent_2 {
## text-indent: 10px;
## }
##
## #sbuahhbmwf .gt_indent_3 {
## text-indent: 15px;
## }
##
## #sbuahhbmwf .gt_indent_4 {
## text-indent: 20px;
## }
##
## #sbuahhbmwf .gt_indent_5 {
## text-indent: 25px;
## }
##
## #sbuahhbmwf .katex-display {
## display: inline-flex !important;
## margin-bottom: 0.75em !important;
## }
##
## #sbuahhbmwf div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
## height: 0px !important;
## }
## </style>
## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Effect of Performance Status (PS) on PFS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">PS</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 0</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 1</td>
## <td headers="estimate" class="gt_row gt_center">1.68</td>
## <td headers="conf.low" class="gt_row gt_center">1.13, 2.49</td>
## <td headers="p.value" class="gt_row gt_center">0.010</td></tr>
## <tr><td headers="label" class="gt_row gt_left"> 2</td>
## <td headers="estimate" class="gt_row gt_center">3.17</td>
## <td headers="conf.low" class="gt_row gt_center">1.52, 6.63</td>
## <td headers="p.value" class="gt_row gt_center">0.002</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
## </div>## Error in `$<-.data.frame`(`*tmp*`, HAP_class, value = structure(integer(0), levels = character(0), class = "factor")): replacement has 0 rows, data has 134## Error in relevel.default(ClinicData$HAP_class, ref = "A"): 'relevel' only for (unordered) factors# Fit the Cox proportional hazards model for Overall Survival
(cox_model_OS_HAP_class <- coxph(Surv(Time_OS, OS_Status) ~ HAP_class, data = ClinicData) %>%
tbl_regression(exp = TRUE)%>%
modify_header(label = "Effect of HAP class on OS "))## Error in eval(predvars, data, env): object 'HAP_class' not found# Fit the Cox proportional hazards model for Progression-Free Survival
(cox_model_PFS_HAP_class <- coxph(Surv(Time_PFS, PFS_Status) ~ HAP_class, data = ClinicData) %>%
tbl_regression(exp = TRUE)%>%
modify_header(label = "Effect of HAP class on PFS "))## Error in eval(predvars, data, env): object 'HAP_class' not foundClinicData$Gender <- factor(ClinicData$Gender)
ClinicData$Gender <- relevel(ClinicData$Gender, ref = "Female")
# Fit the Cox proportional hazards model for Overall Survival
(cox_model_OS_Gender <- coxph(Surv(Time_OS, OS_Status) ~ Gender, data = ClinicData) %>%
tbl_regression(exp = TRUE)%>%
modify_header(label = "Effect of Geneder on OS "))| Effect of Geneder on OS | HR | 95% CI | p-value |
|---|---|---|---|
| Gender | |||
| Female | — | — | |
| Male | 1.31 | 0.73, 2.35 | 0.4 |
| Abbreviations: CI = Confidence Interval, HR = Hazard Ratio | |||
# Fit the Cox proportional hazards model for Progression-Free Survival
(cox_model_PFS_Gender <- coxph(Surv(Time_PFS, PFS_Status) ~ Gender, data = ClinicData) %>%
tbl_regression(exp = TRUE)%>%
modify_header(label = "Effect of Geneder on PFS "))| Effect of Geneder on PFS | HR | 95% CI | p-value |
|---|---|---|---|
| Gender | |||
| Female | — | — | |
| Male | 1.19 | 0.70, 2.02 | 0.5 |
| Abbreviations: CI = Confidence Interval, HR = Hazard Ratio | |||
ClinicData$Cirrhosis <- factor(ClinicData$Cirrhosis)
ClinicData$Cirrhosis <- relevel(ClinicData$Cirrhosis, ref = "NO")
# Fit the Cox proportional hazards model for Overall Survival
(cox_model_OS_Cirrhosis <- coxph(Surv(Time_OS, OS_Status) ~ Cirrhosis, data = ClinicData) %>%
tbl_regression(exp = TRUE)%>%
modify_header(label = "Effect of present of Cirrhosis on OS "))| Effect of present of Cirrhosis on OS | HR | 95% CI | p-value |
|---|---|---|---|
| Cirrhosis | |||
| NO | — | — | |
| Cirrhosis | 0.98 | 0.48, 2.00 | >0.9 |
| YES | 1.48 | 0.77, 2.81 | 0.2 |
| Abbreviations: CI = Confidence Interval, HR = Hazard Ratio | |||
# Fit the Cox proportional hazards model for Progression-Free Survival
(cox_model_PFS_Cirrhosis <- coxph(Surv(Time_PFS, PFS_Status) ~ Cirrhosis, data = ClinicData) %>%
tbl_regression(exp = TRUE)%>%
modify_header(label = "Effect of present of Cirrhosis on PFS "))| Effect of present of Cirrhosis on PFS | HR | 95% CI | p-value |
|---|---|---|---|
| Cirrhosis | |||
| NO | — | — | |
| Cirrhosis | 1.06 | 0.55, 2.06 | 0.9 |
| YES | 1.78 | 0.95, 3.32 | 0.070 |
| Abbreviations: CI = Confidence Interval, HR = Hazard Ratio | |||
library(dplyr)
library(survival)
library(gtsummary)
# Clean/standardize PVT and set reference = "No"
ClinicData <- ClinicData %>%
mutate(
PVT = tolower(trimws(as.character(PVT))),
PVT = case_when(
PVT %in% c("yes","present","1","y","true") ~ "Yes",
PVT %in% c("no","absent","0","n","false") ~ "No",
TRUE ~ tools::toTitleCase(PVT) # keep existing if already "Yes"/"No"
),
PVT = factor(PVT, levels = c("No","Yes")) # 'No' is the reference
)
# Cox PH: Overall Survival
cox_model_OS_PVT <-
coxph(Surv(Time_OS, OS_Status) ~ PVT, data = ClinicData) |>
tbl_regression(exp = TRUE) |>
modify_header(label = "Effect of presence of PVT on OS")
# Cox PH: Progression-Free Survival
cox_model_PFS_PVT <-
coxph(Surv(Time_PFS, PFS_Status) ~ PVT, data = ClinicData) |>
tbl_regression(exp = TRUE) |>
modify_header(label = "Effect of presence of PVT on PFS")
# Print tables
print(cox_model_OS_PVT)## <div id="bnfjofnpoi" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
## <style>#bnfjofnpoi table {
## font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
## -webkit-font-smoothing: antialiased;
## -moz-osx-font-smoothing: grayscale;
## }
##
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## border-style: none;
## }
##
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## margin: 0;
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## color: #333333;
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## background-color: #FFFFFF;
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## color: #333333;
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## color: #333333;
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## margin-top: 0;
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## margin-bottom: 0;
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##
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## padding-top: 8px;
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## color: #333333;
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## color: #333333;
## background-color: #FFFFFF;
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## border-top-width: 2px;
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##
## #bnfjofnpoi .gt_row_group_first th {
## border-top-width: 2px;
## }
##
## #bnfjofnpoi .gt_summary_row {
## color: #333333;
## background-color: #FFFFFF;
## text-transform: inherit;
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
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##
## #bnfjofnpoi .gt_first_summary_row {
## border-top-style: solid;
## border-top-color: #D3D3D3;
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##
## #bnfjofnpoi .gt_first_summary_row.thick {
## border-top-width: 2px;
## }
##
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## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-style: solid;
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## border-bottom-color: #D3D3D3;
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##
## #bnfjofnpoi .gt_grand_summary_row {
## color: #333333;
## background-color: #FFFFFF;
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## padding-top: 8px;
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##
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## padding-top: 8px;
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##
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## padding-top: 8px;
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##
## #bnfjofnpoi .gt_striped {
## background-color: rgba(128, 128, 128, 0.05);
## }
##
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## border-top-style: solid;
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## border-top-color: #D3D3D3;
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##
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## color: #333333;
## background-color: #FFFFFF;
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## border-bottom-color: #D3D3D3;
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## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## }
##
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## margin: 0px;
## font-size: 90%;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
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## }
##
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## color: #333333;
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## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
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##
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## font-size: 90%;
## padding-top: 4px;
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## padding-left: 5px;
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##
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## text-align: left;
## }
##
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## text-align: center;
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##
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## text-align: right;
## font-variant-numeric: tabular-nums;
## }
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## font-weight: normal;
## }
##
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## font-weight: bold;
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##
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## font-style: italic;
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##
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## font-size: 65%;
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##
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## font-size: 75%;
## vertical-align: 0.4em;
## position: initial;
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##
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## font-size: 100%;
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##
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## text-indent: 5px;
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##
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## text-indent: 10px;
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##
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## text-indent: 15px;
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##
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## text-indent: 20px;
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##
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## text-indent: 25px;
## }
##
## #bnfjofnpoi .katex-display {
## display: inline-flex !important;
## margin-bottom: 0.75em !important;
## }
##
## #bnfjofnpoi div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
## height: 0px !important;
## }
## </style>
## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Effect of presence of PVT on OS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">PVT</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> No</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> Yes</td>
## <td headers="estimate" class="gt_row gt_center">2.11</td>
## <td headers="conf.low" class="gt_row gt_center">1.41, 3.16</td>
## <td headers="p.value" class="gt_row gt_center"><0.001</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
## </div>## <div id="uzqyhgvdeg" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
## <style>#uzqyhgvdeg table {
## font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
## -webkit-font-smoothing: antialiased;
## -moz-osx-font-smoothing: grayscale;
## }
##
## #uzqyhgvdeg thead, #uzqyhgvdeg tbody, #uzqyhgvdeg tfoot, #uzqyhgvdeg tr, #uzqyhgvdeg td, #uzqyhgvdeg th {
## border-style: none;
## }
##
## #uzqyhgvdeg p {
## margin: 0;
## padding: 0;
## }
##
## #uzqyhgvdeg .gt_table {
## display: table;
## border-collapse: collapse;
## line-height: normal;
## margin-left: auto;
## margin-right: auto;
## color: #333333;
## font-size: 16px;
## font-weight: normal;
## font-style: normal;
## background-color: #FFFFFF;
## width: auto;
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #A8A8A8;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #A8A8A8;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## }
##
## #uzqyhgvdeg .gt_caption {
## padding-top: 4px;
## padding-bottom: 4px;
## }
##
## #uzqyhgvdeg .gt_title {
## color: #333333;
## font-size: 125%;
## font-weight: initial;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-color: #FFFFFF;
## border-bottom-width: 0;
## }
##
## #uzqyhgvdeg .gt_subtitle {
## color: #333333;
## font-size: 85%;
## font-weight: initial;
## padding-top: 3px;
## padding-bottom: 5px;
## padding-left: 5px;
## padding-right: 5px;
## border-top-color: #FFFFFF;
## border-top-width: 0;
## }
##
## #uzqyhgvdeg .gt_heading {
## background-color: #FFFFFF;
## text-align: center;
## border-bottom-color: #FFFFFF;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## }
##
## #uzqyhgvdeg .gt_bottom_border {
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #uzqyhgvdeg .gt_col_headings {
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## }
##
## #uzqyhgvdeg .gt_col_heading {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: normal;
## text-transform: inherit;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## vertical-align: bottom;
## padding-top: 5px;
## padding-bottom: 6px;
## padding-left: 5px;
## padding-right: 5px;
## overflow-x: hidden;
## }
##
## #uzqyhgvdeg .gt_column_spanner_outer {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: normal;
## text-transform: inherit;
## padding-top: 0;
## padding-bottom: 0;
## padding-left: 4px;
## padding-right: 4px;
## }
##
## #uzqyhgvdeg .gt_column_spanner_outer:first-child {
## padding-left: 0;
## }
##
## #uzqyhgvdeg .gt_column_spanner_outer:last-child {
## padding-right: 0;
## }
##
## #uzqyhgvdeg .gt_column_spanner {
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## vertical-align: bottom;
## padding-top: 5px;
## padding-bottom: 5px;
## overflow-x: hidden;
## display: inline-block;
## width: 100%;
## }
##
## #uzqyhgvdeg .gt_spanner_row {
## border-bottom-style: hidden;
## }
##
## #uzqyhgvdeg .gt_group_heading {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## vertical-align: middle;
## text-align: left;
## }
##
## #uzqyhgvdeg .gt_empty_group_heading {
## padding: 0.5px;
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## vertical-align: middle;
## }
##
## #uzqyhgvdeg .gt_from_md > :first-child {
## margin-top: 0;
## }
##
## #uzqyhgvdeg .gt_from_md > :last-child {
## margin-bottom: 0;
## }
##
## #uzqyhgvdeg .gt_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## margin: 10px;
## border-top-style: solid;
## border-top-width: 1px;
## border-top-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## vertical-align: middle;
## overflow-x: hidden;
## }
##
## #uzqyhgvdeg .gt_stub {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
## border-right-style: solid;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #uzqyhgvdeg .gt_stub_row_group {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
## border-right-style: solid;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## padding-left: 5px;
## padding-right: 5px;
## vertical-align: top;
## }
##
## #uzqyhgvdeg .gt_row_group_first td {
## border-top-width: 2px;
## }
##
## #uzqyhgvdeg .gt_row_group_first th {
## border-top-width: 2px;
## }
##
## #uzqyhgvdeg .gt_summary_row {
## color: #333333;
## background-color: #FFFFFF;
## text-transform: inherit;
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #uzqyhgvdeg .gt_first_summary_row {
## border-top-style: solid;
## border-top-color: #D3D3D3;
## }
##
## #uzqyhgvdeg .gt_first_summary_row.thick {
## border-top-width: 2px;
## }
##
## #uzqyhgvdeg .gt_last_summary_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #uzqyhgvdeg .gt_grand_summary_row {
## color: #333333;
## background-color: #FFFFFF;
## text-transform: inherit;
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #uzqyhgvdeg .gt_first_grand_summary_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-top-style: double;
## border-top-width: 6px;
## border-top-color: #D3D3D3;
## }
##
## #uzqyhgvdeg .gt_last_grand_summary_row_top {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-style: double;
## border-bottom-width: 6px;
## border-bottom-color: #D3D3D3;
## }
##
## #uzqyhgvdeg .gt_striped {
## background-color: rgba(128, 128, 128, 0.05);
## }
##
## #uzqyhgvdeg .gt_table_body {
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #uzqyhgvdeg .gt_footnotes {
## color: #333333;
## background-color: #FFFFFF;
## border-bottom-style: none;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## }
##
## #uzqyhgvdeg .gt_footnote {
## margin: 0px;
## font-size: 90%;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #uzqyhgvdeg .gt_sourcenotes {
## color: #333333;
## background-color: #FFFFFF;
## border-bottom-style: none;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## }
##
## #uzqyhgvdeg .gt_sourcenote {
## font-size: 90%;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #uzqyhgvdeg .gt_left {
## text-align: left;
## }
##
## #uzqyhgvdeg .gt_center {
## text-align: center;
## }
##
## #uzqyhgvdeg .gt_right {
## text-align: right;
## font-variant-numeric: tabular-nums;
## }
##
## #uzqyhgvdeg .gt_font_normal {
## font-weight: normal;
## }
##
## #uzqyhgvdeg .gt_font_bold {
## font-weight: bold;
## }
##
## #uzqyhgvdeg .gt_font_italic {
## font-style: italic;
## }
##
## #uzqyhgvdeg .gt_super {
## font-size: 65%;
## }
##
## #uzqyhgvdeg .gt_footnote_marks {
## font-size: 75%;
## vertical-align: 0.4em;
## position: initial;
## }
##
## #uzqyhgvdeg .gt_asterisk {
## font-size: 100%;
## vertical-align: 0;
## }
##
## #uzqyhgvdeg .gt_indent_1 {
## text-indent: 5px;
## }
##
## #uzqyhgvdeg .gt_indent_2 {
## text-indent: 10px;
## }
##
## #uzqyhgvdeg .gt_indent_3 {
## text-indent: 15px;
## }
##
## #uzqyhgvdeg .gt_indent_4 {
## text-indent: 20px;
## }
##
## #uzqyhgvdeg .gt_indent_5 {
## text-indent: 25px;
## }
##
## #uzqyhgvdeg .katex-display {
## display: inline-flex !important;
## margin-bottom: 0.75em !important;
## }
##
## #uzqyhgvdeg div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
## height: 0px !important;
## }
## </style>
## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Effect of presence of PVT on PFS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">PVT</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> No</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> Yes</td>
## <td headers="estimate" class="gt_row gt_center">2.90</td>
## <td headers="conf.low" class="gt_row gt_center">1.96, 4.28</td>
## <td headers="p.value" class="gt_row gt_center"><0.001</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
## </div>library(dplyr)
library(stringr)
library(survival)
library(gtsummary)
# Factor + reference
ClinicData$Treatment_Class <- factor(ClinicData$Treatment_Class)
if ("TKI" %in% levels(ClinicData$Treatment_Class)) {
ClinicData$Treatment_Class <- relevel(ClinicData$Treatment_Class, ref = "TKI")
}
# Cox models
library(survival)
library(gtsummary)
cox_model_OS_Treatment_Class <-
coxph(Surv(Time_OS, OS_Status) ~ Treatment_Class, data = ClinicData) |>
tbl_regression(exp = TRUE) |>
modify_header(label = "Effect of Treatment Class on OS")
cox_model_PFS_Treatment_Class <-
coxph(Surv(Time_PFS, PFS_Status) ~ Treatment_Class, data = ClinicData) |>
tbl_regression(exp = TRUE) |>
modify_header(label = "Effect of Treatment Class on PFS")
print(cox_model_OS_Treatment_Class)## <div id="zsmtykoesu" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
## <style>#zsmtykoesu table {
## font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
## -webkit-font-smoothing: antialiased;
## -moz-osx-font-smoothing: grayscale;
## }
##
## #zsmtykoesu thead, #zsmtykoesu tbody, #zsmtykoesu tfoot, #zsmtykoesu tr, #zsmtykoesu td, #zsmtykoesu th {
## border-style: none;
## }
##
## #zsmtykoesu p {
## margin: 0;
## padding: 0;
## }
##
## #zsmtykoesu .gt_table {
## display: table;
## border-collapse: collapse;
## line-height: normal;
## margin-left: auto;
## margin-right: auto;
## color: #333333;
## font-size: 16px;
## font-weight: normal;
## font-style: normal;
## background-color: #FFFFFF;
## width: auto;
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #A8A8A8;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #A8A8A8;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## }
##
## #zsmtykoesu .gt_caption {
## padding-top: 4px;
## padding-bottom: 4px;
## }
##
## #zsmtykoesu .gt_title {
## color: #333333;
## font-size: 125%;
## font-weight: initial;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-color: #FFFFFF;
## border-bottom-width: 0;
## }
##
## #zsmtykoesu .gt_subtitle {
## color: #333333;
## font-size: 85%;
## font-weight: initial;
## padding-top: 3px;
## padding-bottom: 5px;
## padding-left: 5px;
## padding-right: 5px;
## border-top-color: #FFFFFF;
## border-top-width: 0;
## }
##
## #zsmtykoesu .gt_heading {
## background-color: #FFFFFF;
## text-align: center;
## border-bottom-color: #FFFFFF;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## }
##
## #zsmtykoesu .gt_bottom_border {
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #zsmtykoesu .gt_col_headings {
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## }
##
## #zsmtykoesu .gt_col_heading {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: normal;
## text-transform: inherit;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## vertical-align: bottom;
## padding-top: 5px;
## padding-bottom: 6px;
## padding-left: 5px;
## padding-right: 5px;
## overflow-x: hidden;
## }
##
## #zsmtykoesu .gt_column_spanner_outer {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: normal;
## text-transform: inherit;
## padding-top: 0;
## padding-bottom: 0;
## padding-left: 4px;
## padding-right: 4px;
## }
##
## #zsmtykoesu .gt_column_spanner_outer:first-child {
## padding-left: 0;
## }
##
## #zsmtykoesu .gt_column_spanner_outer:last-child {
## padding-right: 0;
## }
##
## #zsmtykoesu .gt_column_spanner {
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## vertical-align: bottom;
## padding-top: 5px;
## padding-bottom: 5px;
## overflow-x: hidden;
## display: inline-block;
## width: 100%;
## }
##
## #zsmtykoesu .gt_spanner_row {
## border-bottom-style: hidden;
## }
##
## #zsmtykoesu .gt_group_heading {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## vertical-align: middle;
## text-align: left;
## }
##
## #zsmtykoesu .gt_empty_group_heading {
## padding: 0.5px;
## color: #333333;
## background-color: #FFFFFF;
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## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Effect of Treatment Class on OS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">Treatment_Class</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> TKI</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> ICI</td>
## <td headers="estimate" class="gt_row gt_center">0.71</td>
## <td headers="conf.low" class="gt_row gt_center">0.45, 1.12</td>
## <td headers="p.value" class="gt_row gt_center">0.14</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
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## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Effect of Treatment Class on PFS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">Treatment_Class</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> TKI</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> ICI</td>
## <td headers="estimate" class="gt_row gt_center">0.67</td>
## <td headers="conf.low" class="gt_row gt_center">0.45, 1.00</td>
## <td headers="p.value" class="gt_row gt_center">0.048</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
## </div>library(dplyr)
library(survival)
library(gtsummary)
# ✅ 1. Create a clean binary variable (≤10% = low, >10% = high)
ClinicData <- ClinicData %>%
mutate(
cfDNA_TF_Baseline_binary = ifelse(Tumour_Fraction_ichorCNA_Baseline > 0.10, "high", "low"),
cfDNA_TF_Baseline_binary = factor(cfDNA_TF_Baseline_binary, levels = c("low", "high"))
)
# ✅ 2. Fit Cox model for Overall Survival (OS)
cox_model_OS_TumourFraction <- coxph(Surv(Time_OS, OS_Status) ~ cfDNA_TF_Baseline_binary, data = ClinicData) %>%
tbl_regression(exp = TRUE) %>%
modify_header(label = "Tumour Fraction >10% (high) vs ≤10% (low) – OS")
# ✅ 3. Fit Cox model for Progression-Free Survival (PFS)
cox_model_PFS_TumourFraction <- coxph(Surv(Time_PFS, PFS_Status) ~ cfDNA_TF_Baseline_binary, data = ClinicData) %>%
tbl_regression(exp = TRUE) %>%
modify_header(label = "Tumour Fraction >10% (high) vs ≤10% (low) – PFS")
# ✅ 4. Print the results to R Markdown console/output
print(cox_model_OS_TumourFraction)## <div id="mwxleixrgh" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
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## -webkit-font-smoothing: antialiased;
## -moz-osx-font-smoothing: grayscale;
## }
##
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## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Tumour Fraction >10% (high) vs ≤10% (low) – OS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
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## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">cfDNA_TF_Baseline_binary</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> low</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> high</td>
## <td headers="estimate" class="gt_row gt_center">1.62</td>
## <td headers="conf.low" class="gt_row gt_center">1.06, 2.47</td>
## <td headers="p.value" class="gt_row gt_center">0.024</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
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## font-weight: initial;
## text-transform: inherit;
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
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## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
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## text-align: left;
## }
##
## #joeejwrqzq .gt_empty_group_heading {
## padding: 0.5px;
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## vertical-align: middle;
## }
##
## #joeejwrqzq .gt_from_md > :first-child {
## margin-top: 0;
## }
##
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## margin-bottom: 0;
## }
##
## #joeejwrqzq .gt_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## margin: 10px;
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## border-top-width: 1px;
## border-top-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 1px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## vertical-align: middle;
## overflow-x: hidden;
## }
##
## #joeejwrqzq .gt_stub {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
## border-right-style: solid;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #joeejwrqzq .gt_stub_row_group {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
## border-right-style: solid;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
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## }
##
## #joeejwrqzq .gt_row_group_first td {
## border-top-width: 2px;
## }
##
## #joeejwrqzq .gt_row_group_first th {
## border-top-width: 2px;
## }
##
## #joeejwrqzq .gt_summary_row {
## color: #333333;
## background-color: #FFFFFF;
## text-transform: inherit;
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #joeejwrqzq .gt_first_summary_row {
## border-top-style: solid;
## border-top-color: #D3D3D3;
## }
##
## #joeejwrqzq .gt_first_summary_row.thick {
## border-top-width: 2px;
## }
##
## #joeejwrqzq .gt_last_summary_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
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## border-bottom-color: #D3D3D3;
## }
##
## #joeejwrqzq .gt_grand_summary_row {
## color: #333333;
## background-color: #FFFFFF;
## text-transform: inherit;
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #joeejwrqzq .gt_first_grand_summary_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-top-style: double;
## border-top-width: 6px;
## border-top-color: #D3D3D3;
## }
##
## #joeejwrqzq .gt_last_grand_summary_row_top {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-style: double;
## border-bottom-width: 6px;
## border-bottom-color: #D3D3D3;
## }
##
## #joeejwrqzq .gt_striped {
## background-color: rgba(128, 128, 128, 0.05);
## }
##
## #joeejwrqzq .gt_table_body {
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
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## color: #333333;
## background-color: #FFFFFF;
## border-bottom-style: none;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## }
##
## #joeejwrqzq .gt_footnote {
## margin: 0px;
## font-size: 90%;
## padding-top: 4px;
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## padding-left: 5px;
## padding-right: 5px;
## }
##
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## color: #333333;
## background-color: #FFFFFF;
## border-bottom-style: none;
## border-bottom-width: 2px;
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## border-left-width: 2px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## }
##
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## font-size: 90%;
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## }
##
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## text-align: left;
## }
##
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## text-align: center;
## }
##
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## text-align: right;
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##
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##
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## font-style: italic;
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##
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##
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##
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##
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##
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## text-indent: 10px;
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##
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## text-indent: 15px;
## }
##
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## text-indent: 20px;
## }
##
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## text-indent: 25px;
## }
##
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## display: inline-flex !important;
## margin-bottom: 0.75em !important;
## }
##
## #joeejwrqzq div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
## height: 0px !important;
## }
## </style>
## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Tumour Fraction >10% (high) vs ≤10% (low) – PFS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">cfDNA_TF_Baseline_binary</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> low</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> high</td>
## <td headers="estimate" class="gt_row gt_center">2.13</td>
## <td headers="conf.low" class="gt_row gt_center">1.43, 3.16</td>
## <td headers="p.value" class="gt_row gt_center"><0.001</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
## </div># ───────────────────────────────────────────────────────────────
# Cox proportional hazards model for tMAD (binary: >0.02 vs ≤0.02)
# ───────────────────────────────────────────────────────────────
library(dplyr)
library(survival)
library(gtsummary)
# 1️⃣ Create binary variable based on threshold 0.02
ClinicData <- ClinicData %>%
mutate(
tMAD_binary = ifelse(tMAD > 0.02, "high", "low"),
tMAD_binary = factor(tMAD_binary, levels = c("low", "high")) # 'low' as reference
)
# Sanity check
table(ClinicData$tMAD_binary, useNA = "ifany")##
## low high
## 95 39# 2️⃣ Cox proportional hazards model for Overall Survival (OS)
cox_model_OS_tMAD <- coxph(Surv(Time_OS, OS_Status) ~ tMAD_binary, data = ClinicData) %>%
tbl_regression(exp = TRUE) %>%
modify_header(label = "tMAD score > 0.02 ('high') versus ≤ 0.02 ('low') on OS")
# 3️⃣ Cox proportional hazards model for Progression-Free Survival (PFS)
cox_model_PFS_tMAD <- coxph(Surv(Time_PFS, PFS_Status) ~ tMAD_binary, data = ClinicData) %>%
tbl_regression(exp = TRUE) %>%
modify_header(label = "tMAD score > 0.02 ('high') versus ≤ 0.02 ('low') on PFS")
# 4️⃣ Print both results to the R Markdown output
print(cox_model_OS_tMAD)## <div id="wizkkddhsf" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
## <style>#wizkkddhsf table {
## font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
## -webkit-font-smoothing: antialiased;
## -moz-osx-font-smoothing: grayscale;
## }
##
## #wizkkddhsf thead, #wizkkddhsf tbody, #wizkkddhsf tfoot, #wizkkddhsf tr, #wizkkddhsf td, #wizkkddhsf th {
## border-style: none;
## }
##
## #wizkkddhsf p {
## margin: 0;
## padding: 0;
## }
##
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## display: table;
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## line-height: normal;
## margin-left: auto;
## margin-right: auto;
## color: #333333;
## font-size: 16px;
## font-weight: normal;
## font-style: normal;
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##
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##
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## font-size: 85%;
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## padding-bottom: 5px;
## padding-left: 5px;
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##
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## background-color: #FFFFFF;
## text-align: center;
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## border-left-style: none;
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## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## }
##
## #wizkkddhsf .gt_bottom_border {
## border-bottom-style: solid;
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## border-bottom-color: #D3D3D3;
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##
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## border-right-style: none;
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## border-right-color: #D3D3D3;
## }
##
## #wizkkddhsf .gt_col_heading {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: normal;
## text-transform: inherit;
## border-left-style: none;
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## padding-right: 5px;
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## }
##
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## font-size: 100%;
## font-weight: normal;
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## padding-right: 4px;
## }
##
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##
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##
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##
## #wizkkddhsf .gt_spanner_row {
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##
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## vertical-align: middle;
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##
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##
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##
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##
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## border-top-color: #D3D3D3;
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## border-right-style: none;
## border-right-width: 1px;
## border-right-color: #D3D3D3;
## vertical-align: middle;
## overflow-x: hidden;
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##
## #wizkkddhsf .gt_stub {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
## border-right-style: solid;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #wizkkddhsf .gt_stub_row_group {
## color: #333333;
## background-color: #FFFFFF;
## font-size: 100%;
## font-weight: initial;
## text-transform: inherit;
## border-right-style: solid;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## padding-left: 5px;
## padding-right: 5px;
## vertical-align: top;
## }
##
## #wizkkddhsf .gt_row_group_first td {
## border-top-width: 2px;
## }
##
## #wizkkddhsf .gt_row_group_first th {
## border-top-width: 2px;
## }
##
## #wizkkddhsf .gt_summary_row {
## color: #333333;
## background-color: #FFFFFF;
## text-transform: inherit;
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #wizkkddhsf .gt_first_summary_row {
## border-top-style: solid;
## border-top-color: #D3D3D3;
## }
##
## #wizkkddhsf .gt_first_summary_row.thick {
## border-top-width: 2px;
## }
##
## #wizkkddhsf .gt_last_summary_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #wizkkddhsf .gt_grand_summary_row {
## color: #333333;
## background-color: #FFFFFF;
## text-transform: inherit;
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #wizkkddhsf .gt_first_grand_summary_row {
## padding-top: 8px;
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## padding-left: 5px;
## padding-right: 5px;
## border-top-style: double;
## border-top-width: 6px;
## border-top-color: #D3D3D3;
## }
##
## #wizkkddhsf .gt_last_grand_summary_row_top {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-style: double;
## border-bottom-width: 6px;
## border-bottom-color: #D3D3D3;
## }
##
## #wizkkddhsf .gt_striped {
## background-color: rgba(128, 128, 128, 0.05);
## }
##
## #wizkkddhsf .gt_table_body {
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #wizkkddhsf .gt_footnotes {
## color: #333333;
## background-color: #FFFFFF;
## border-bottom-style: none;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
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## height: 0px !important;
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## </style>
## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>tMAD score > 0.02 (‘high’) versus ≤ 0.02 (‘low’) on OS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">tMAD_binary</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> low</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> high</td>
## <td headers="estimate" class="gt_row gt_center">1.52</td>
## <td headers="conf.low" class="gt_row gt_center">0.97, 2.38</td>
## <td headers="p.value" class="gt_row gt_center">0.065</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
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## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>tMAD score > 0.02 (‘high’) versus ≤ 0.02 (‘low’) on PFS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">tMAD_binary</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> low</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> high</td>
## <td headers="estimate" class="gt_row gt_center">1.28</td>
## <td headers="conf.low" class="gt_row gt_center">0.85, 1.93</td>
## <td headers="p.value" class="gt_row gt_center">0.2</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
## </div>library(dplyr)
library(survival)
library(gtsummary)
# 1) Create binary from DELFI_Baseline (threshold 0.4)
ClinicData <- ClinicData %>%
mutate(
DELFI_binary = ifelse(DELFI_Baseline > 0.287, "high", "low"),
DELFI_binary = factor(DELFI_binary, levels = c("low","high")) # 'low' = reference
)
# optional sanity check
# table(ClinicData$DELFI_binary, useNA = "ifany")
# 2) Cox models
label_OS <- "DELFI score > 0.4 ('high') versus ≤ 0.4 ('low') on OS"
label_PFS <- "DELFI score > 0.4 ('high') versus ≤ 0.4 ('low') on PFS"
cox_model_OS_DELFI <-
coxph(Surv(Time_OS, OS_Status) ~ DELFI_binary, data = ClinicData) %>%
tbl_regression(exp = TRUE) %>%
modify_header(label = label_OS)
cox_model_PFS_DELFI <-
coxph(Surv(Time_PFS, PFS_Status) ~ DELFI_binary, data = ClinicData) %>%
tbl_regression(exp = TRUE) %>%
modify_header(label = label_PFS)
# 3) Print results
print(cox_model_OS_DELFI)## <div id="vfyakpvewb" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
## <style>#vfyakpvewb table {
## font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
## -webkit-font-smoothing: antialiased;
## -moz-osx-font-smoothing: grayscale;
## }
##
## #vfyakpvewb thead, #vfyakpvewb tbody, #vfyakpvewb tfoot, #vfyakpvewb tr, #vfyakpvewb td, #vfyakpvewb th {
## border-style: none;
## }
##
## #vfyakpvewb p {
## margin: 0;
## padding: 0;
## }
##
## #vfyakpvewb .gt_table {
## display: table;
## border-collapse: collapse;
## line-height: normal;
## margin-left: auto;
## margin-right: auto;
## color: #333333;
## font-size: 16px;
## font-weight: normal;
## font-style: normal;
## background-color: #FFFFFF;
## width: auto;
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #A8A8A8;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #A8A8A8;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
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## padding-bottom: 4px;
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## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>DELFI score > 0.4 (‘high’) versus ≤ 0.4 (‘low’) on OS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
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## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
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## <tr><td headers="label" class="gt_row gt_left"> low</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
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## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
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## border-top-width: 6px;
## border-top-color: #D3D3D3;
## }
##
## #kngvpdyoel .gt_last_grand_summary_row_top {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-style: double;
## border-bottom-width: 6px;
## border-bottom-color: #D3D3D3;
## }
##
## #kngvpdyoel .gt_striped {
## background-color: rgba(128, 128, 128, 0.05);
## }
##
## #kngvpdyoel .gt_table_body {
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #kngvpdyoel .gt_footnotes {
## color: #333333;
## background-color: #FFFFFF;
## border-bottom-style: none;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## }
##
## #kngvpdyoel .gt_footnote {
## margin: 0px;
## font-size: 90%;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #kngvpdyoel .gt_sourcenotes {
## color: #333333;
## background-color: #FFFFFF;
## border-bottom-style: none;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## }
##
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## font-size: 90%;
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## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
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##
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## text-align: left;
## }
##
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## text-align: center;
## }
##
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## text-align: right;
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## }
##
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## font-weight: normal;
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##
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##
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## font-style: italic;
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## text-indent: 20px;
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##
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## text-indent: 25px;
## }
##
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## display: inline-flex !important;
## margin-bottom: 0.75em !important;
## }
##
## #kngvpdyoel div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
## height: 0px !important;
## }
## </style>
## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>DELFI score > 0.4 (‘high’) versus ≤ 0.4 (‘low’) on PFS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">DELFI_binary</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> low</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> high</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
## </div>library(dplyr)
library(survival)
library(gtsummary)
# 1) Build binary from the numeric column `cfDNA_Level_Baseline_ng_ml`
ClinicData <- ClinicData %>%
mutate(
cfDNA_Level_Baseline_binary =
ifelse(as.numeric(cfDNA_Level_Baseline_ng_ml) > 1200, "high", "low"),
cfDNA_Level_Baseline_binary =
factor(cfDNA_Level_Baseline_binary, levels = c("low","high")) # 'low' ref
)
# 2) Cox models
label_OS <- "Effect of Plasma cfDNA level (ng/mL) > 1200 ('high') versus ≤ 1200 ('low') on OS"
label_PFS <- "Effect of Plasma cfDNA level (ng/mL) > 1200 ('high') versus ≤ 1200 ('low') on PFS"
cox_model_OS_cfDNAlevels <-
coxph(Surv(Time_OS, OS_Status) ~ cfDNA_Level_Baseline_binary, data = ClinicData) |>
tbl_regression(exp = TRUE) |>
modify_header(label = label_OS)
cox_model_PFS_cfDNAlevels <-
coxph(Surv(Time_PFS, PFS_Status) ~ cfDNA_Level_Baseline_binary, data = ClinicData) |>
tbl_regression(exp = TRUE) |>
modify_header(label = label_PFS)
# 3) Print results
print(cox_model_OS_cfDNAlevels)## <div id="khnpzezqeu" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
## <style>#khnpzezqeu table {
## font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
## -webkit-font-smoothing: antialiased;
## -moz-osx-font-smoothing: grayscale;
## }
##
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## border-style: none;
## }
##
## #khnpzezqeu p {
## margin: 0;
## padding: 0;
## }
##
## #khnpzezqeu .gt_table {
## display: table;
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## color: #333333;
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## border-left-style: none;
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## border-bottom-style: solid;
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## border-right-color: #D3D3D3;
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## color: #333333;
## background-color: #FFFFFF;
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## color: #333333;
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## border-left-width: 1px;
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## border-right-width: 1px;
## border-right-color: #D3D3D3;
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##
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## color: #333333;
## background-color: #FFFFFF;
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## color: #333333;
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##
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## color: #333333;
## background-color: #FFFFFF;
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## padding-right: 5px;
## }
##
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## border-top-style: solid;
## border-top-color: #D3D3D3;
## }
##
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## border-top-width: 2px;
## }
##
## #khnpzezqeu .gt_last_summary_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #khnpzezqeu .gt_grand_summary_row {
## color: #333333;
## background-color: #FFFFFF;
## text-transform: inherit;
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #khnpzezqeu .gt_first_grand_summary_row {
## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-top-style: double;
## border-top-width: 6px;
## border-top-color: #D3D3D3;
## }
##
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## padding-top: 8px;
## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## border-bottom-style: double;
## border-bottom-width: 6px;
## border-bottom-color: #D3D3D3;
## }
##
## #khnpzezqeu .gt_striped {
## background-color: rgba(128, 128, 128, 0.05);
## }
##
## #khnpzezqeu .gt_table_body {
## border-top-style: solid;
## border-top-width: 2px;
## border-top-color: #D3D3D3;
## border-bottom-style: solid;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## }
##
## #khnpzezqeu .gt_footnotes {
## color: #333333;
## background-color: #FFFFFF;
## border-bottom-style: none;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
## border-left-width: 2px;
## border-left-color: #D3D3D3;
## border-right-style: none;
## border-right-width: 2px;
## border-right-color: #D3D3D3;
## }
##
## #khnpzezqeu .gt_footnote {
## margin: 0px;
## font-size: 90%;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
## #khnpzezqeu .gt_sourcenotes {
## color: #333333;
## background-color: #FFFFFF;
## border-bottom-style: none;
## border-bottom-width: 2px;
## border-bottom-color: #D3D3D3;
## border-left-style: none;
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## border-right-style: none;
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## border-right-color: #D3D3D3;
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##
## #khnpzezqeu .gt_sourcenote {
## font-size: 90%;
## padding-top: 4px;
## padding-bottom: 4px;
## padding-left: 5px;
## padding-right: 5px;
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##
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## text-align: left;
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##
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## text-align: center;
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##
## #khnpzezqeu .gt_right {
## text-align: right;
## font-variant-numeric: tabular-nums;
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## font-weight: normal;
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## font-weight: bold;
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## font-style: italic;
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##
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## font-size: 65%;
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## font-size: 75%;
## vertical-align: 0.4em;
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## font-size: 100%;
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## text-indent: 15px;
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##
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## text-indent: 20px;
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##
## #khnpzezqeu .gt_indent_5 {
## text-indent: 25px;
## }
##
## #khnpzezqeu .katex-display {
## display: inline-flex !important;
## margin-bottom: 0.75em !important;
## }
##
## #khnpzezqeu div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
## height: 0px !important;
## }
## </style>
## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Effect of Plasma cfDNA level (ng/mL) > 1200 (‘high’) versus ≤ 1200 (‘low’) on OS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">cfDNA_Level_Baseline_binary</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> low</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> high</td>
## <td headers="estimate" class="gt_row gt_center">1.10</td>
## <td headers="conf.low" class="gt_row gt_center">0.74, 1.65</td>
## <td headers="p.value" class="gt_row gt_center">0.6</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
## </div>## <div id="holiodycts" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
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## <thead>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'>Effect of Plasma cfDNA level (ng/mL) > 1200 (‘high’) versus ≤ 1200 (‘low’) on PFS</span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>HR</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">cfDNA_Level_Baseline_binary</td>
## <td headers="estimate" class="gt_row gt_center"><br /></td>
## <td headers="conf.low" class="gt_row gt_center"><br /></td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> low</td>
## <td headers="estimate" class="gt_row gt_center">—</td>
## <td headers="conf.low" class="gt_row gt_center">—</td>
## <td headers="p.value" class="gt_row gt_center"><br /></td></tr>
## <tr><td headers="label" class="gt_row gt_left"> high</td>
## <td headers="estimate" class="gt_row gt_center">1.18</td>
## <td headers="conf.low" class="gt_row gt_center">0.82, 1.72</td>
## <td headers="p.value" class="gt_row gt_center">0.4</td></tr>
## </tbody>
## <tfoot>
## <tr class="gt_sourcenotes">
## <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, HR = Hazard Ratio</span></td>
## </tr>
## </tfoot>
## </table>
## </div>Summary of Univariable Cox Regression Analysis Results:
The univariable Cox regression analysis identified several significant predictors based on a p-value threshold of < 0.05. The significant predictors are as follows:
1) For Overall Survival (OS):
2) For Progression-Free Survival (PFS):
#Create a List of Significant Predictors for OS
#For predictors showing significance in univariable analysis:
significant_predictors_OS <- c("AFP_binary", "BCLC", "PS",
"PVT", "PS", "DELFI_binary", "cfDNA_TF_Baseline_binary","CTP")
#Create a List of Significant Predictors for PFS
#For predictors showing significance in univariable analysis:
significant_predictors_PFS <- c("AFP_binary", "BCLC", "PS",
"PVT", "PS", "DELFI_binary", "cfDNA_TF_Baseline_binary","CTP")
#####Multivariable analysis#####
# Fit the multivariable Cox proportional hazards model
#Using the significant predictors identified from the univariable analysis,
#We can fit a multivariable Cox proportional hazards model:
(Final_model_OS <- coxph(Surv(Time_OS, OS_Status) ~ AFP_binary + BCLC +
+ PS + PVT + DELFI_binary + cfDNA_TF_Baseline_binary + CTP , data = ClinicData))## Call:
## coxph(formula = Surv(Time_OS, OS_Status) ~ AFP_binary + BCLC +
## +PS + PVT + DELFI_binary + cfDNA_TF_Baseline_binary + CTP,
## data = ClinicData)
##
## coef exp(coef) se(coef) z p
## AFP_binaryhigh 1.02315 2.78194 0.25087 4.078 4.53e-05
## BCLCC 0.77743 2.17587 0.34121 2.278 0.0227
## PS1 -0.08683 0.91684 0.30634 -0.283 0.7769
## PS2 0.22257 1.24928 0.47782 0.466 0.6414
## PVTYes 0.16082 1.17448 0.33020 0.487 0.6262
## DELFI_binaryhigh NA NA 0.00000 NA NA
## cfDNA_TF_Baseline_binaryhigh 0.15499 1.16765 0.23382 0.663 0.5074
## CTPB 0.22180 1.24833 0.31019 0.715 0.4746
##
## Likelihood ratio test=40.08 on 7 df, p=1.217e-06
## n= 134, number of events= 100## Call:
## coxph(formula = Surv(Time_OS, OS_Status) ~ AFP_binary + BCLC +
## +PS + PVT + DELFI_binary + cfDNA_TF_Baseline_binary + CTP,
## data = ClinicData)
##
## n= 134, number of events= 100
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AFP_binaryhigh 1.02315 2.78194 0.25087 4.078 4.53e-05 ***
## BCLCC 0.77743 2.17587 0.34121 2.278 0.0227 *
## PS1 -0.08683 0.91684 0.30634 -0.283 0.7769
## PS2 0.22257 1.24928 0.47782 0.466 0.6414
## PVTYes 0.16082 1.17448 0.33020 0.487 0.6262
## DELFI_binaryhigh NA NA 0.00000 NA NA
## cfDNA_TF_Baseline_binaryhigh 0.15499 1.16765 0.23382 0.663 0.5074
## CTPB 0.22180 1.24833 0.31019 0.715 0.4746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AFP_binaryhigh 2.7819 0.3595 1.7014 4.549
## BCLCC 2.1759 0.4596 1.1148 4.247
## PS1 0.9168 1.0907 0.5030 1.671
## PS2 1.2493 0.8005 0.4897 3.187
## PVTYes 1.1745 0.8514 0.6149 2.243
## DELFI_binaryhigh NA NA NA NA
## cfDNA_TF_Baseline_binaryhigh 1.1676 0.8564 0.7384 1.846
## CTPB 1.2483 0.8011 0.6797 2.293
##
## Concordance= 0.701 (se = 0.027 )
## Likelihood ratio test= 40.08 on 7 df, p=1e-06
## Wald test = 41.04 on 7 df, p=8e-07
## Score (logrank) test = 46.39 on 7 df, p=7e-08(Final_model_PFS <- coxph(Surv(Time_PFS, PFS_Status) ~ AFP_binary + BCLC +
+ PS + PVT + DELFI_binary + cfDNA_TF_Baseline_binary + CTP + Treatment_Class, data = ClinicData))## Call:
## coxph(formula = Surv(Time_PFS, PFS_Status) ~ AFP_binary + BCLC +
## +PS + PVT + DELFI_binary + cfDNA_TF_Baseline_binary + CTP +
## Treatment_Class, data = ClinicData)
##
## coef exp(coef) se(coef) z p
## AFP_binaryhigh 0.4791 1.6147 0.2354 2.036 0.04177
## BCLCC 0.8271 2.2868 0.3577 2.312 0.02077
## PS1 -0.4217 0.6559 0.2985 -1.413 0.15779
## PS2 -0.2744 0.7600 0.4642 -0.591 0.55443
## PVTYes 0.6413 1.8990 0.4263 1.504 0.13247
## DELFI_binaryhigh NA NA 0.0000 NA NA
## cfDNA_TF_Baseline_binaryhigh 0.5843 1.7937 0.2192 2.666 0.00769
## CTPB 0.4317 1.5399 0.2958 1.460 0.14439
## Treatment_ClassICI -0.1130 0.8932 0.2875 -0.393 0.69445
##
## Likelihood ratio test=53.14 on 8 df, p=1.014e-08
## n= 134, number of events= 114## Call:
## coxph(formula = Surv(Time_PFS, PFS_Status) ~ AFP_binary + BCLC +
## +PS + PVT + DELFI_binary + cfDNA_TF_Baseline_binary + CTP +
## Treatment_Class, data = ClinicData)
##
## n= 134, number of events= 114
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AFP_binaryhigh 0.4791 1.6147 0.2354 2.036 0.04177 *
## BCLCC 0.8271 2.2868 0.3577 2.312 0.02077 *
## PS1 -0.4217 0.6559 0.2985 -1.413 0.15779
## PS2 -0.2744 0.7600 0.4642 -0.591 0.55443
## PVTYes 0.6413 1.8990 0.4263 1.504 0.13247
## DELFI_binaryhigh NA NA 0.0000 NA NA
## cfDNA_TF_Baseline_binaryhigh 0.5843 1.7937 0.2192 2.666 0.00769 **
## CTPB 0.4317 1.5399 0.2958 1.460 0.14439
## Treatment_ClassICI -0.1130 0.8932 0.2875 -0.393 0.69445
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AFP_binaryhigh 1.6147 0.6193 1.0180 2.561
## BCLCC 2.2868 0.4373 1.1342 4.610
## PS1 0.6559 1.5245 0.3654 1.178
## PS2 0.7600 1.3157 0.3060 1.888
## PVTYes 1.8990 0.5266 0.8235 4.379
## DELFI_binaryhigh NA NA NA NA
## cfDNA_TF_Baseline_binaryhigh 1.7937 0.5575 1.1673 2.756
## CTPB 1.5399 0.6494 0.8624 2.750
## Treatment_ClassICI 0.8932 1.1196 0.5084 1.569
##
## Concordance= 0.71 (se = 0.024 )
## Likelihood ratio test= 53.14 on 8 df, p=1e-08
## Wald test = 51.03 on 8 df, p=3e-08
## Score (logrank) test = 56.58 on 8 df, p=2e-09(cox <- coxph(Surv(Time_OS, OS_Status) ~ AFP_binary + BCLC +
+ PS + PVT + DELFI_binary + cfDNA_TF_Baseline_binary + CTP, data = ClinicData) %>%
tbl_regression(exp = TRUE))| Characteristic | HR | 95% CI | p-value |
|---|---|---|---|
| AFP_binary | |||
| low | — | — | |
| high | 2.78 | 1.70, 4.55 | <0.001 |
| BCLC | |||
| B | — | — | |
| C | 2.18 | 1.11, 4.25 | 0.023 |
| PS | |||
| 0 | — | — | |
| 1 | 0.92 | 0.50, 1.67 | 0.8 |
| 2 | 1.25 | 0.49, 3.19 | 0.6 |
| PVT | |||
| No | — | — | |
| Yes | 1.17 | 0.61, 2.24 | 0.6 |
| DELFI_binary | |||
| low | — | — | |
| high | |||
| cfDNA_TF_Baseline_binary | |||
| low | — | — | |
| high | 1.17 | 0.74, 1.85 | 0.5 |
| CTP | |||
| A | — | — | |
| B | 1.25 | 0.68, 2.29 | 0.5 |
| Abbreviations: CI = Confidence Interval, HR = Hazard Ratio | |||
(cox <- coxph(Surv(Time_PFS, PFS_Status) ~ AFP_binary + BCLC +
+ PS + PVT + DELFI_binary + cfDNA_TF_Baseline_binary + CTP + Treatment_Class, data = ClinicData) %>%
tbl_regression(exp = TRUE))| Characteristic | HR | 95% CI | p-value |
|---|---|---|---|
| AFP_binary | |||
| low | — | — | |
| high | 1.61 | 1.02, 2.56 | 0.042 |
| BCLC | |||
| B | — | — | |
| C | 2.29 | 1.13, 4.61 | 0.021 |
| PS | |||
| 0 | — | — | |
| 1 | 0.66 | 0.37, 1.18 | 0.2 |
| 2 | 0.76 | 0.31, 1.89 | 0.6 |
| PVT | |||
| No | — | — | |
| Yes | 1.90 | 0.82, 4.38 | 0.13 |
| DELFI_binary | |||
| low | — | — | |
| high | |||
| cfDNA_TF_Baseline_binary | |||
| low | — | — | |
| high | 1.79 | 1.17, 2.76 | 0.008 |
| CTP | |||
| A | — | — | |
| B | 1.54 | 0.86, 2.75 | 0.14 |
| Treatment_Class | |||
| TKI | — | — | |
| ICI | 0.89 | 0.51, 1.57 | 0.7 |
| Abbreviations: CI = Confidence Interval, HR = Hazard Ratio | |||
#Check the proportional hazards assumption
#We can check the proportional hazards assumption for the final model using the cox.zph function:
ph_test_OS <- cox.zph(Final_model_OS)
print(ph_test_OS)## chisq df p
## AFP_binary 0.0404 1 0.84076
## BCLC 2.7314 1 0.09839
## PS 1.4897 2 0.47480
## PVT 0.0162 1 0.89859
## cfDNA_TF_Baseline_binary 12.5557 1 0.00039
## CTP 0.0699 1 0.79150
## GLOBAL 18.3264 7 0.01058## chisq df p
## AFP_binary 0.305 1 0.5810
## BCLC 1.786 1 0.1814
## PS 2.437 2 0.2956
## PVT 0.315 1 0.5745
## cfDNA_TF_Baseline_binary 7.312 1 0.0068
## CTP 1.255 1 0.2627
## Treatment_Class 1.599 1 0.2060
## GLOBAL 14.494 8 0.0698# Fit the Cox proportional hazards model
cox_model_Age <- coxph(Surv(ClinicData$Age, ClinicData$OS_Status) ~ Age, data = ClinicData)
# Display the model summary
summary(cox_model_Age)## Call:
## coxph(formula = Surv(ClinicData$Age, ClinicData$OS_Status) ~
## Age, data = ClinicData)
##
## n= 134, number of events= 100
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Age -1.756e+01 2.369e-08 5.885e+02 -0.03 0.976
##
## exp(coef) exp(-coef) lower .95 upper .95
## Age 2.369e-08 42214101 0 Inf
##
## Concordance= 0.992 (se = 0.002 )
## Likelihood ratio test= 560.4 on 1 df, p=<2e-16
## Wald test = 0 on 1 df, p=1
## Score (logrank) test = 227.9 on 1 df, p=<2e-16The coefficient: in a statistical model,like the Cox proportional hazards model, represents the effect of a variable (e.g., Age) on the outcome (e.g., risk of death), where a negative coefficient suggests that as the variable increases, the risk decreases and versa.
Hazard Ratio (exp(coef)): for Age: is approximately 2.54e-08, which is an extremely small value, indicating that the risk of death decreases significantly with each additional year of age. However, this value is so small that it might not be realistic or interpretable in a practical sense.
Standard Error (se(coef)): The standard error of the coefficient is very large (approximately 719.7), suggesting a high degree of uncertainty in the estimate of the coefficient for Age.
The z-score is -0.024, and p-value is 0.981. The very high p-value indicates that the coefficient for Age is not statistically significant. This means that we cannot conclude that Age has a significant effect on the risk of death based on this model.
The confidence interval for the hazard ratio ranges from 0 to infinity. This extremely wide interval further indicates a high degree of uncertainty and suggests that the model is not providing a precise estimate of the effect of Age on the risk of death.
The concordance statistic is 0.995, which suggests that the model has a very high ability to correctly rank the risk of death between patients. However, given the other statistics, this might not be reliable.
The likelihood ratio test and score (logrank) test have very low p-values (<2e-16), suggesting that the model overall is statistically significant. The Wald test, however, has a p-value of 1, indicating that the coefficient for Age itself is not statistically significant.